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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| library_name
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RuudVelo/wav2vec2-large-xls-r-300m-cv8-nl
|
RuudVelo
| 2022-03-24T11:53:26Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"nl",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- nl
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-cv8-nl
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: nl
metrics:
- name: Test WER
type: wer
value: 14.53
- name: Test CER
type: cer
value: 4.7
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: nl
metrics:
- name: Test WER
type: wer
value: 33.7
- name: Test CER
type: cer
value: 15.64
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: nl
metrics:
- name: Test WER
type: wer
value: 35.19
---
<!-- 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-cv8-nl
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. In addition a 6gram KenLM model was trained and used. The KenLM model was based on train+validation Common Voice 8
It achieves results depicted on the rigth side on the model card (testset CV8)
## Model description
Dutch wav2vec2-xls-r-300m model using Common Voice 8 dataset
## Intended uses & limitations
More information needed
## Training and evaluation data
The model was trained on Dutch common voice 8 with 75 epochs. The train set consisted of the common voice 8 train set and evaluation set was the common voice 8 validation set. The WER reported is on the common voice 8 test set which was not part of training nor validation (eval)
## Training procedure
### Training hyperparameters
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.1
- Tokenizers 0.11.0
|
RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B
|
RASMUS
| 2022-03-24T11:53:21Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"mozilla-foundation/common_voice_7_0",
"audio",
"speech",
"robust-speech-event",
"hf-asr-leaderboard",
"fi",
"dataset:mozilla-foundation/common_voice_7_0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language: fi
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
- cer
tags:
- generated_from_trainer
- mozilla-foundation/common_voice_7_0
- audio
- automatic-speech-recognition
- speech
- robust-speech-event
- hf-asr-leaderboard
model-index:
- name: XLS-R 1B Wav2Vec2 Finnish by Rasmus Toivanen
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: fi
metrics:
- name: Test WER
type: wer
value: 10.96
- name: Test CER
type: cer
value: 2.81
---
<!-- 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-xlsr-fi-train-aug-lm-1B
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1499
- Wer: 0.1955
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6473 | 0.29 | 400 | 0.2857 | 0.3825 |
| 0.6039 | 0.58 | 800 | 0.2459 | 0.3476 |
| 0.4757 | 0.87 | 1200 | 0.2338 | 0.3274 |
| 0.4473 | 1.15 | 1600 | 0.2246 | 0.3128 |
| 0.4322 | 1.44 | 2000 | 0.1962 | 0.2805 |
| 0.3961 | 1.73 | 2400 | 0.2070 | 0.2797 |
| 0.3642 | 2.02 | 2800 | 0.1790 | 0.2473 |
| 0.3561 | 2.31 | 3200 | 0.1769 | 0.2375 |
| 0.282 | 2.6 | 3600 | 0.1672 | 0.2263 |
| 0.2978 | 2.89 | 4000 | 0.1636 | 0.2192 |
| 0.2722 | 3.17 | 4400 | 0.1637 | 0.2102 |
| 0.2924 | 3.46 | 4800 | 0.1506 | 0.2021 |
| 0.2631 | 3.75 | 5200 | 0.1499 | 0.1955 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
Mofe/xls-r-hausa-40
|
Mofe
| 2022-03-24T11:53:10Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ha
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: ha
metrics:
- name: Test WER
type: wer
value: 51.31
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4998
- Wer: 0.5153
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 9.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 80.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0021 | 8.33 | 500 | 2.9059 | 1.0 |
| 2.6604 | 16.66 | 1000 | 2.6402 | 0.9892 |
| 1.2216 | 24.99 | 1500 | 0.6051 | 0.6851 |
| 1.0754 | 33.33 | 2000 | 0.5408 | 0.6464 |
| 0.9582 | 41.66 | 2500 | 0.5521 | 0.5935 |
| 0.8653 | 49.99 | 3000 | 0.5156 | 0.5550 |
| 0.7867 | 58.33 | 3500 | 0.5439 | 0.5606 |
| 0.7265 | 66.66 | 4000 | 0.4863 | 0.5255 |
| 0.6699 | 74.99 | 4500 | 0.5050 | 0.5169 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-pa-IN-dx1
|
DrishtiSharma
| 2022-03-24T11:52:59Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"pa-IN",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- pa-IN
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- pa-IN
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-pa-IN-dx1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pa-IN
metrics:
- name: Test WER
type: wer
value: 0.48725989807918463
- name: Test CER
type: cer
value: 0.1687305197540224
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pa-IN
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0855
- Wer: 0.4755
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-pa-IN-dx1 --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Punjabi language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1200
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4607 | 9.26 | 500 | 2.7746 | 1.0416 |
| 0.3442 | 18.52 | 1000 | 0.9114 | 0.5911 |
| 0.2213 | 27.78 | 1500 | 0.9687 | 0.5751 |
| 0.1242 | 37.04 | 2000 | 1.0204 | 0.5461 |
| 0.0998 | 46.3 | 2500 | 1.0250 | 0.5233 |
| 0.0727 | 55.56 | 3000 | 1.1072 | 0.5382 |
| 0.0605 | 64.81 | 3500 | 1.0588 | 0.5073 |
| 0.0458 | 74.07 | 4000 | 1.0818 | 0.5069 |
| 0.0338 | 83.33 | 4500 | 1.0948 | 0.5108 |
| 0.0223 | 92.59 | 5000 | 1.0986 | 0.4775 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-kk-with-LM
|
DrishtiSharma
| 2022-03-24T11:52:57Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"kk",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- kk
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- kk
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-kk-with-LM
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ru
metrics:
- name: Test WER
type: wer
value: 0.4355
- name: Test CER
type: cer
value: 0.10469915859660263
- name: Test WER (+LM)
type: wer
value: 0.417
- name: Test CER (+LM)
type: cer
value: 0.10319098269566598
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: kk
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: kk
metrics:
- name: Test WER
type: wer
value: 41.7
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: kk
metrics:
- name: Test WER
type: wer
value: 67.09
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - KK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7149
- Wer: 0.451
# Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-kk-with-LM --dataset mozilla-foundation/common_voice_8_0 --config kk --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Kazakh language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000222
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 9.6799 | 9.09 | 200 | 3.6119 | 1.0 |
| 3.1332 | 18.18 | 400 | 2.5352 | 1.005 |
| 1.0465 | 27.27 | 600 | 0.6169 | 0.682 |
| 0.3452 | 36.36 | 800 | 0.6572 | 0.607 |
| 0.2575 | 45.44 | 1000 | 0.6527 | 0.578 |
| 0.2088 | 54.53 | 1200 | 0.6828 | 0.551 |
| 0.158 | 63.62 | 1400 | 0.7074 | 0.5575 |
| 0.1309 | 72.71 | 1600 | 0.6523 | 0.5595 |
| 0.1074 | 81.8 | 1800 | 0.7262 | 0.5415 |
| 0.087 | 90.89 | 2000 | 0.7199 | 0.521 |
| 0.0711 | 99.98 | 2200 | 0.7113 | 0.523 |
| 0.0601 | 109.09 | 2400 | 0.6863 | 0.496 |
| 0.0451 | 118.18 | 2600 | 0.6998 | 0.483 |
| 0.0378 | 127.27 | 2800 | 0.6971 | 0.4615 |
| 0.0319 | 136.36 | 3000 | 0.7119 | 0.4475 |
| 0.0305 | 145.44 | 3200 | 0.7181 | 0.459 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
### Evaluation Command
!python eval.py \
--model_id DrishtiSharma/wav2vec2-xls-r-300m-kk-n2 \
--dataset mozilla-foundation/common_voice_8_0 --config kk --split test --log_outputs
|
DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2
|
DrishtiSharma
| 2022-03-24T11:52:52Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"hi",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-hi-cv8-b2
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 7
args: hi
metrics:
- type: wer
value: 0.3891350503092403
name: Test WER
- name: Test CER
type: cer
value: 0.13016327327131985
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hi
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- 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-hi-cv8-b2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7322
- Wer: 0.3469
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Hindi language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.6226 | 1.04 | 200 | 3.8855 | 1.0 |
| 3.4678 | 2.07 | 400 | 3.4283 | 1.0 |
| 2.3668 | 3.11 | 600 | 1.0743 | 0.7175 |
| 0.7308 | 4.15 | 800 | 0.7663 | 0.5498 |
| 0.4985 | 5.18 | 1000 | 0.6957 | 0.5001 |
| 0.3817 | 6.22 | 1200 | 0.6932 | 0.4866 |
| 0.3281 | 7.25 | 1400 | 0.7034 | 0.4983 |
| 0.2752 | 8.29 | 1600 | 0.6588 | 0.4606 |
| 0.2475 | 9.33 | 1800 | 0.6514 | 0.4328 |
| 0.219 | 10.36 | 2000 | 0.6396 | 0.4176 |
| 0.2036 | 11.4 | 2200 | 0.6867 | 0.4162 |
| 0.1793 | 12.44 | 2400 | 0.6943 | 0.4196 |
| 0.1724 | 13.47 | 2600 | 0.6862 | 0.4260 |
| 0.1554 | 14.51 | 2800 | 0.7615 | 0.4222 |
| 0.151 | 15.54 | 3000 | 0.7058 | 0.4110 |
| 0.1335 | 16.58 | 3200 | 0.7172 | 0.3986 |
| 0.1326 | 17.62 | 3400 | 0.7182 | 0.3923 |
| 0.1225 | 18.65 | 3600 | 0.6995 | 0.3910 |
| 0.1146 | 19.69 | 3800 | 0.7075 | 0.3875 |
| 0.108 | 20.73 | 4000 | 0.7297 | 0.3858 |
| 0.1048 | 21.76 | 4200 | 0.7413 | 0.3850 |
| 0.0979 | 22.8 | 4400 | 0.7452 | 0.3793 |
| 0.0946 | 23.83 | 4600 | 0.7436 | 0.3759 |
| 0.0897 | 24.87 | 4800 | 0.7289 | 0.3754 |
| 0.0854 | 25.91 | 5000 | 0.7271 | 0.3667 |
| 0.0803 | 26.94 | 5200 | 0.7378 | 0.3656 |
| 0.0752 | 27.98 | 5400 | 0.7488 | 0.3680 |
| 0.0718 | 29.02 | 5600 | 0.7185 | 0.3619 |
| 0.0702 | 30.05 | 5800 | 0.7428 | 0.3554 |
| 0.0653 | 31.09 | 6000 | 0.7447 | 0.3559 |
| 0.0638 | 32.12 | 6200 | 0.7327 | 0.3523 |
| 0.058 | 33.16 | 6400 | 0.7339 | 0.3488 |
| 0.0594 | 34.2 | 6600 | 0.7322 | 0.3469 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-tatar
|
infinitejoy
| 2022-03-24T11:52:33Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"tt",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- tt
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- tt
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Tatar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: tt
metrics:
- name: Test WER
type: wer
value: 24.392
- name: Test CER
type: cer
value: 5.024
---
<!-- 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-tatar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - TT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1959
- Wer: 0.2454
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.173 | 9.66 | 4000 | 0.2920 | 0.3608 |
| 0.9433 | 19.32 | 8000 | 0.2336 | 0.3026 |
| 0.8552 | 28.99 | 12000 | 0.2221 | 0.2799 |
| 0.7863 | 38.65 | 16000 | 0.1953 | 0.2479 |
| 0.7365 | 48.31 | 20000 | 0.1968 | 0.2449 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-kyrgyz
|
infinitejoy
| 2022-03-24T11:52:31Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ky",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ky
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ky
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Kyrgyz
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ky
metrics:
- name: Test WER
type: wer
value: 40.908
- name: Test CER
type: cer
value: 10.999
---
<!-- 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-kyrgyz
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - KY dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5817
- Wer: 0.4096
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.5412 | 18.69 | 2000 | 0.6161 | 0.5747 |
| 1.311 | 37.38 | 4000 | 0.5707 | 0.5070 |
| 1.1367 | 56.07 | 6000 | 0.5372 | 0.4664 |
| 0.9696 | 74.77 | 8000 | 0.5443 | 0.4328 |
| 0.8163 | 93.46 | 10000 | 0.5916 | 0.4124 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-irish
|
infinitejoy
| 2022-03-24T11:52:28Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ga-IE",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ga-IE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ga-IE
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Irish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ga-IE
metrics:
- name: Test WER
type: wer
value: 103.54
- name: Test CER
type: cer
value: 326.923
---
<!-- 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-irish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GA-IE dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1647
- Wer: 0.7296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 300.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.9022 | 124.94 | 500 | 2.7763 | 0.9824 |
| 1.5112 | 249.94 | 1000 | 1.1736 | 0.7405 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
glob-asr/wav2vec2-large-xls-r-300m-guarani-small
|
glob-asr
| 2022-03-24T11:52:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"gn",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- gn
license: apache-2.0
tags:
- generated_from_trainer
- robust-speech-event
- gn
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-guarani-small
results: []
---
<!-- 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-guarani-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: 0.4964
- Wer: 0.5957
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 6.65 | 100 | 1.1326 | 1.0 |
| 1.6569 | 13.32 | 200 | 0.5264 | 0.6478 |
| 1.6569 | 19.97 | 300 | 0.5370 | 0.6261 |
| 0.2293 | 26.65 | 400 | 0.4964 | 0.5957 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
comodoro/wav2vec2-xls-r-300m-pl-cv8
|
comodoro
| 2022-03-24T11:52:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"pl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- xlsr-fine-tuning-week
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: Polish comodoro Wav2Vec2 XLSR 300M CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pl
metrics:
- name: Test WER
type: wer
value: 17.0
- name: Test CER
type: cer
value: 3.8
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pl
metrics:
- name: Test WER
type: wer
value: 38.97
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pl
metrics:
- name: Test WER
type: wer
value: 46.05
---
# wav2vec2-xls-r-300m-pl-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 8.0 dataset.
It achieves the following results on the evaluation set while training:
- Loss: 0.1716
- Wer: 0.1697
- Cer: 0.0385
The `eval.py` script results are:
WER: 0.16970531733661967
CER: 0.03839135416519316
## Model description
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
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("mozilla-foundation/common_voice_8_0", "pl", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-pl-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config pl
```
## Training and evaluation data
The Common Voice 8.0 `train` and `validation` datasets were used for training
## Training procedure
### Training hyperparameters
The following hyperparameters were used:
- learning_rate: 1e-4
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
- mixed_precision_training: Native AMP
The training was interrupted after 3250 steps.
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
comodoro/wav2vec2-xls-r-300m-cs-cv8
|
comodoro
| 2022-03-24T11:52:03Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"cs",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- cs
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- xlsr-fine-tuning-week
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Czech comodoro Wav2Vec2 XLSR 300M CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: cs
metrics:
- name: Test WER
type: wer
value: 10.3
- name: Test CER
type: cer
value: 2.6
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: cs
metrics:
- name: Test WER
type: wer
value: 54.29
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: cs
metrics:
- name: Test WER
type: wer
value: 44.55
---
<!-- 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-cs-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 8.0 dataset.
It achieves the following results on the evaluation set while training:
- Loss: 0.2327
- Wer: 0.1608
- Cer: 0.0376
The `eval.py` script results using a LM are:
WER: 0.10281503199350225
CER: 0.02622802241689026
## Model description
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
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("mozilla-foundation/common_voice_8_0", "cs", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-cv8")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-cv8")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-cs-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config cs
```
## Training and evaluation data
The Common Voice 8.0 `train` and `validation` datasets were used for training
## Training procedure
### Training hyperparameters
The following hyperparameters were used during first stage of training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
- mixed_precision_training: Native AMP
The following hyperparameters were used during second stage of training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 7.2926 | 8.06 | 250 | 3.8497 | 1.0 | 1.0 |
| 3.417 | 16.13 | 500 | 3.2852 | 1.0 | 0.9857 |
| 2.0264 | 24.19 | 750 | 0.7099 | 0.7342 | 0.1768 |
| 0.4018 | 32.25 | 1000 | 0.6188 | 0.6415 | 0.1551 |
| 0.2444 | 40.32 | 1250 | 0.6632 | 0.6362 | 0.1600 |
| 0.1882 | 48.38 | 1500 | 0.6070 | 0.5783 | 0.1388 |
| 0.153 | 56.44 | 1750 | 0.6425 | 0.5720 | 0.1377 |
| 0.1214 | 64.51 | 2000 | 0.6363 | 0.5546 | 0.1337 |
| 0.1011 | 72.57 | 2250 | 0.6310 | 0.5222 | 0.1224 |
| 0.0879 | 80.63 | 2500 | 0.6353 | 0.5258 | 0.1253 |
| 0.0782 | 88.7 | 2750 | 0.6078 | 0.4904 | 0.1127 |
| 0.0709 | 96.76 | 3000 | 0.6465 | 0.4960 | 0.1154 |
| 0.0661 | 104.82 | 3250 | 0.6622 | 0.4945 | 0.1166 |
| 0.0616 | 112.89 | 3500 | 0.6440 | 0.4786 | 0.1104 |
| 0.0579 | 120.95 | 3750 | 0.6815 | 0.4887 | 0.1144 |
| 0.0549 | 129.03 | 4000 | 0.6603 | 0.4780 | 0.1105 |
| 0.0527 | 137.09 | 4250 | 0.6652 | 0.4749 | 0.1090 |
| 0.0506 | 145.16 | 4500 | 0.6958 | 0.4846 | 0.1133 |
Further fine-tuning with slightly different architecture and higher learning rate:
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.576 | 8.06 | 250 | 0.2411 | 0.2340 | 0.0502 |
| 0.2564 | 16.13 | 500 | 0.2305 | 0.2097 | 0.0492 |
| 0.2018 | 24.19 | 750 | 0.2371 | 0.2059 | 0.0494 |
| 0.1549 | 32.25 | 1000 | 0.2298 | 0.1844 | 0.0435 |
| 0.1224 | 40.32 | 1250 | 0.2288 | 0.1725 | 0.0407 |
| 0.1004 | 48.38 | 1500 | 0.2327 | 0.1608 | 0.0376 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
lsb/wav2vec2-base-it-latin
|
lsb
| 2022-03-24T11:51:21Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"la",
"dataset:lsb/poetaexmachina-mp3-recitations",
"license:agpl-3.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- la
license: agpl-3.0
tags:
- robust-speech-event
- hf-asr-leaderboard
datasets:
- lsb/poetaexmachina-mp3-recitations
metrics:
- wer
model-index:
- name: wav2vec2-base-it-latin
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: lsb/poetaexmachina-mp3-recitations
name: Poeta Ex Machina mp3 recitations
metrics:
- type: wer
value: 0.398
name: Test WER
---
---
# wav2vec2-base-it-latin
This model is a fine-tuned version of [wav2vec2-base-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-base-it-voxpopuli)
The dataset used is the [poetaexmachina-mp3-recitations](https://github.com/lsb/poetaexmachina-mp3-recitations),
all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's [database](https://github.com/lsb/poetaexmachina/blob/master/merged-scansions.db) of words with scansions).
It achieves the following [results](https://github.com/lsb/tironiculum/blame/trunk/wav2vec2%20base%20it%20latin.ipynb#L1234) on the evaluation set:
- Loss: 0.1943
- WER: 0.398
|
sammy786/wav2vec2-xlsr-lithuanian
|
sammy786
| 2022-03-24T11:49:34Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"lt",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- lt
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- lt
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-lithuanian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: lt
metrics:
- name: Test WER
type: wer
value: 14.67
- name: Test CER
type: cer
value: 2.77
---
# sammy786/wav2vec2-xlsr-lithuanian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - lt dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 13.1811
- Wer: 24.2570
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 8
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:-----:|:-------------:|:---------------:|:--------:|
| 200 | 5.718700 | 2.897032 | 1.000000 |
| 400 | 1.340000 | 0.309548 | 0.507284 |
| 600 | 0.799100 | 0.220205 | 0.402098 |
| 800 | 0.494400 | 0.185093 | 0.352855 |
| 1000 | 0.370800 | 0.165869 | 0.334207 |
| 1200 | 0.312500 | 0.159801 | 0.324009 |
| 1400 | 0.276100 | 0.148066 | 0.321678 |
| 1600 | 0.250100 | 0.153748 | 0.311626 |
| 1800 | 0.226400 | 0.147437 | 0.302885 |
| 2000 | 0.206900 | 0.141176 | 0.296037 |
| 2200 | 0.189900 | 0.142161 | 0.288170 |
| 2400 | 0.192100 | 0.138029 | 0.286568 |
| 2600 | 0.175600 | 0.139496 | 0.283654 |
| 2800 | 0.156900 | 0.138609 | 0.283217 |
| 3000 | 0.149400 | 0.140468 | 0.281906 |
| 3200 | 0.144600 | 0.132472 | 0.278263 |
| 3400 | 0.144100 | 0.141028 | 0.277535 |
| 3600 | 0.133000 | 0.134287 | 0.275495 |
| 3800 | 0.126600 | 0.149136 | 0.277681 |
| 4000 | 0.123500 | 0.132180 | 0.266463 |
| 4200 | 0.113000 | 0.137942 | 0.268211 |
| 4400 | 0.111700 | 0.140038 | 0.272873 |
| 4600 | 0.108600 | 0.136756 | 0.264132 |
| 4800 | 0.103600 | 0.137541 | 0.263403 |
| 5000 | 0.098000 | 0.140435 | 0.264860 |
| 5200 | 0.095800 | 0.136950 | 0.262383 |
| 5400 | 0.094000 | 0.128214 | 0.263986 |
| 5600 | 0.085300 | 0.125024 | 0.259761 |
| 5800 | 0.078900 | 0.128575 | 0.260198 |
| 6000 | 0.083300 | 0.135496 | 0.258887 |
| 6200 | 0.078800 | 0.131706 | 0.259178 |
| 6400 | 0.073800 | 0.128451 | 0.255390 |
| 6600 | 0.072600 | 0.131245 | 0.252768 |
| 6800 | 0.073300 | 0.131525 | 0.249417 |
| 7000 | 0.069000 | 0.128627 | 0.255536 |
| 7200 | 0.064400 | 0.127767 | 0.250583 |
| 7400 | 0.065400 | 0.129557 | 0.247815 |
| 7600 | 0.061200 | 0.129734 | 0.250146 |
| 7800 | 0.059100 | 0.135124 | 0.249709 |
| 8000 | 0.057000 | 0.132850 | 0.249126 |
| 8200 | 0.056100 | 0.128827 | 0.248252 |
| 8400 | 0.056400 | 0.130229 | 0.246795 |
| 8600 | 0.052800 | 0.128939 | 0.245775 |
| 8800 | 0.051100 | 0.131892 | 0.248543 |
| 9000 | 0.052900 | 0.132062 | 0.244464 |
| 9200 | 0.048200 | 0.130988 | 0.244172 |
| 9400 | 0.047700 | 0.131811 | 0.242570 |
| 9600 | 0.050000 | 0.133832 | 0.245484 |
| 9800 | 0.047500 | 0.134340 | 0.243881 |
| 10000 | 0.048400 | 0.133388 | 0.243590 |
| 10200 | 0.047800 | 0.132729 | 0.244464 |
| 10400 | 0.049000 | 0.131695 | 0.245047 |
| 10600 | 0.044400 | 0.132154 | 0.245484 |
| 10800 | 0.050100 | 0.131575 | 0.245192 |
| 11000 | 0.047700 | 0.131211 | 0.245192 |
| 11200 | 0.046000 | 0.131293 | 0.245047 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-lithuanian --dataset mozilla-foundation/common_voice_8_0 --config lt --split test
```
|
joe5campbell/Horovod_Tweet_Sentiment_1k_3eps
|
joe5campbell
| 2022-03-24T11:48:32Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-24T11:48:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Horovod_Tweet_Sentiment_1k_3eps
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Horovod_Tweet_Sentiment_1k_3eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6961535
- Train Accuracy: 0.49375
- Validation Loss: 0.6676211
- Validation Accuracy: 0.64375
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 0.0003, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.717013 | 0.46562502 | 0.73462963 | 0.515625 | 0 |
| 0.70586157 | 0.5078125 | 0.6937375 | 0.484375 | 1 |
| 0.6961535 | 0.49375 | 0.6676211 | 0.64375 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.6.0
- Tokenizers 0.11.6
|
infinitejoy/wav2vec2-large-xls-r-300m-bulgarian
|
infinitejoy
| 2022-03-24T11:47:30Z | 445 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"bg",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bg
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- bg
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Bulgarian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: bg
metrics:
- name: Test WER
type: wer
value: 46.68
- name: Test CER
type: cer
value: 10.75
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: bg
metrics:
- name: Test WER
type: wer
value: 63.68
- name: Test CER
type: cer
value: 19.88
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: bg
metrics:
- name: Test WER
type: wer
value: 64.08
---
<!-- 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-bulgarian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4487
- Wer: 0.4674
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9774 | 6.33 | 500 | 2.9769 | 1.0 |
| 1.3453 | 12.66 | 1000 | 0.6523 | 0.6980 |
| 1.1658 | 18.99 | 1500 | 0.5636 | 0.6359 |
| 1.0797 | 25.32 | 2000 | 0.5004 | 0.5759 |
| 1.044 | 31.65 | 2500 | 0.4958 | 0.5569 |
| 0.9915 | 37.97 | 3000 | 0.4971 | 0.5350 |
| 0.9429 | 44.3 | 3500 | 0.4829 | 0.5229 |
| 0.9266 | 50.63 | 4000 | 0.4515 | 0.5074 |
| 0.8965 | 56.96 | 4500 | 0.4599 | 0.5039 |
| 0.878 | 63.29 | 5000 | 0.4735 | 0.4954 |
| 0.8494 | 69.62 | 5500 | 0.4460 | 0.4878 |
| 0.8343 | 75.95 | 6000 | 0.4510 | 0.4795 |
| 0.8236 | 82.28 | 6500 | 0.4538 | 0.4789 |
| 0.8069 | 88.61 | 7000 | 0.4526 | 0.4748 |
| 0.7958 | 94.94 | 7500 | 0.4496 | 0.4700 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner
|
JustAdvanceTechonology
| 2022-03-24T11:33:03Z | 5 | 4 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-24T10:20:14Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0269
- Validation Loss: 0.0551
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1775 | 0.0646 | 0 |
| 0.0454 | 0.0580 | 1 |
| 0.0269 | 0.0551 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.5.0
- Datasets 1.18.3
- Tokenizers 0.11.6
|
buvnswrn/daml-t5-pretrain-imdb-accelerate
|
buvnswrn
| 2022-03-24T11:22:52Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T11:06:02Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- imdb
model-index:
- name: daml-t5-pretrain-imdb
results: []
---
<!-- 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. -->
# daml-t5-pretrain-imdb
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
niksmer/PolicyBERTa-7d
|
niksmer
| 2022-03-24T09:19:57Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
model-index:
- name: PolicyBERTa-7d
results: []
widget:
- text: "Russia must end the war."
- text: "Democratic institutions must be supported."
- text: "The state must fight political corruption."
- text: "Our energy economy must be nationalised."
- text: "We must increase social spending."
---
# PolicyBERTa-7d
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). It was inspired by the model from [Laurer (2020)](https://huggingface.co/MoritzLaurer/policy-distilbert-7d).
It achieves the following results on the evaluation set:
- Loss: 0.8549
- Accuracy: 0.7059
- F1-micro: 0.7059
- F1-macro: 0.6683
- F1-weighted: 0.7033
- Precision: 0.7059
- Recall: 0.7059
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of seven political categories: "external relations", "freedom and democracy", "political system", "economy", "welfare and quality of life", "fabric of society" and "social groups".
## Intended uses & limitations
The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance.
```python
from transformers import pipeline
import pandas as pd
classifier = pipeline(
task="text-classification",
model="niksmer/PolicyBERTa-7d")
# Load text data you want to classify
text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list()
# Inference
output = classifier(text)
# Print output
pd.DataFrame(output).head()
```
## Training and evaluation data
PolicyBERTa-7d was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020.
| Country | Count manifestos | Count sentences | Time span |
|----------------|------------------|-----------------|--------------------|
| Australia | 18 | 14,887 | 2010-2016 |
| Ireland | 23 | 24,966 | 2007-2016 |
| Canada | 14 | 12,344 | 2004-2008 & 2015 |
| New Zealand | 46 | 35,079 | 1993-2017 |
| South Africa | 29 | 13,334 | 1994-2019 |
| USA | 9 | 13,188 | 1992 & 2004-2020 |
| United Kingdom | 34 | 30,936 | 1997-2019 |
Canadian manifestos between 2004 and 2008 are used as test data.
The Manifesto Project mannually annotates individual sentences from political party manifestos in 7 main political domains: 'Economy', 'External Relations', 'Fabric of Society', 'Freedom and Democracy', 'Political System', 'Welfare and Quality of Life' or 'Social Groups' - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each domain.
### Tain data
Train data was higly imbalanced.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 7,640 |
| 1 | freedom and democracy | 5,880 |
| 2 | political system | 11,234 |
| 3 | economy | 29,218 |
| 4 | welfare and quality of life | 37,200 |
| 5 | fabric of society | 13,594 |
| 6 | social groups | 11,177 |
Overall count: 115,943
### Validation data
The validation was created by chance.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 1,345 |
| 1 | freedom and democracy | 1,043 |
| 2 | political system | 2,038 |
| 3 | economy | 5,140 |
| 4 | welfare and quality of life | 6,554 |
| 5 | fabric of society | 2,384 |
| 6 | social groups | 1,957 |
Overall count: 20,461
## Test data
The test dataset contains ten canadian manifestos between 2004 and 2008.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 824 |
| 1 | freedom and democracy | 296 |
| 2 | political system | 1,041 |
| 3 | economy | 2,188 |
| 4 | welfare and quality of life | 2,654 |
| 5 | fabric of society | 940 |
| 6 | social groups | 387 |
Overall count: 8,330
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
```
training_args = TrainingArguments(
warmup_steps=0,
weight_decay=0.1,
learning_rate=1e-05,
fp16 = True,
evaluation_strategy="epoch",
num_train_epochs=5,
per_device_train_batch_size=16,
overwrite_output_dir=True,
per_device_eval_batch_size=16,
save_strategy="no",
logging_dir='logs',
logging_strategy= 'steps',
logging_steps=10,
push_to_hub=True,
hub_strategy="end")
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 0.9154 | 1.0 | 1812 | 0.8984 | 0.6785 | 0.6785 | 0.6383 | 0.6772 | 0.6785 | 0.6785 |
| 0.8374 | 2.0 | 3624 | 0.8569 | 0.6957 | 0.6957 | 0.6529 | 0.6914 | 0.6957 | 0.6957 |
| 0.7053 | 3.0 | 5436 | 0.8582 | 0.7019 | 0.7019 | 0.6594 | 0.6967 | 0.7019 | 0.7019 |
| 0.7178 | 4.0 | 7248 | 0.8488 | 0.7030 | 0.7030 | 0.6662 | 0.7011 | 0.7030 | 0.7030 |
| 0.6688 | 5.0 | 9060 | 0.8549 | 0.7059 | 0.7059 | 0.6683 | 0.7033 | 0.7059 | 0.7059 |
### Validation evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| PolicyBERTa-7d | 0.71 | 0.67 | 0.70 |
### Test evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| PolicyBERTa-7d | 0.65 | 0.60 | 0.65 |
### Evaluation per category
| Label | Validation F1-Score | Test F1-Score |
|-----------------------------|---------------------|---------------|
| external relations | 0.76 | 0.70 |
| freedom and democracy | 0.61 | 0.55 |
| political system | 0.55 | 0.55 |
| economy | 0.74 | 0.67 |
| welfare and quality of life | 0.77 | 0.72 |
| fabric of society | 0.67 | 0.60 |
| social groups | 0.58 | 0.41 |
### Evaluation based on saliency theory
Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent.
Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology.
The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions). But PolicyBERTa isn't fine-tuned to predict the rile-index, if you're interested in that, check [ManiBERT](https://huggingface.co/niksmer/ManiBERT) or [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE).
In the following table, the predicted and original share of the individual policy domains are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original shares is 0.965.
| Party-ID | Year | Type | Share external relations | Share freedom and democracy | Share political system | Share economy | Share welfare and quality of life | Share fabric of society | Share social groups |
|--------------|-------------|---------------|--------------------------|-----------------------------|------------------------|----------------|-----------------------------------|-------------------------|---------------------|
| 62320 | 2004 | Predicted | 7.1% | 4.8% | 13.2% | 20.3% | 35.2% | 9.6% | 9.8% |
| | | Original | 10.2% | 2.5% | 13.7% | 23.8% | 31.7% | 11.6% | 6.4% |
| 62320 | 2006 | Predicted | 2.9% | 4.7% | 16.4% | 18.9% | 38.3% | 11.9% | 6.9% |
| | | Original | 5.6% | 5.0% | 15.8% | 20.7% | 38.7% | 9.3% | 4.9% |
| 62320 | 2008 | Predicted | 6.8% | 4.7% | 6.2% | 24.7% | 38.3% | 10.3% | 9.0% |
| | | Original | 5.6% | 3.7% | 8.2% | 33.1% | 29.5% | 11.7% | 4.3% |
| 62420 | 2004 | Predicted | 9.7% | 3.5% | 14.5% | 24.7% | 34.8% | 8.5% | 4.3% |
| | | Original | 12.6% | 1.3% | 18.8% | 23.0% | 33.2% | 9.0% | 2.0% |
| 62420 | 2006 | Predicted | 9.5% | 2.2% | 7.9% | 27.8% | 34.8% | 9.2% | 8.7% |
| | | Original | 10.6% | 2.5% | 9.6% | 29.7% | 33.1% | 8.3% | 6.2% |
| 62420 | 2008 | Predicted | 0.7% | 0.5% | 3.5% | 41.7% | 46.4% | 3.7% | 3.5% |
| | | Original | 2.0% | 0.2% | 4.4% | 33.3% | 45.9% | 7.7% | 6.4% |
| 62623 | 2004 | Predicted | 7.1% | 11.4% | 24.5% | 17.6% | 21.5% | 13.6% | 4.3% |
| | | Original | 8.4% | 6.7% | 28.8% | 17.4% | 18.7% | 15.5% | 4.5% |
| 62623 | 2006 | Predicted | 5.6% | 8.5% | 23.6% | 15.6% | 14.8% | 24.3% | 7.6% |
| | | Original | 5.0% | 8.9% | 22.2% | 17.4% | 17.2% | 25.7% | 3.6% |
| 62623 | 2008 | Predicted | 5.0% | 4.4% | 12.2% | 33.1% | 21.9% | 17.5% | 5.9% |
| | | Original | 5.6% | 2.2% | 11.6% | 37.8% | 17.8% | 20.9% | 4.1% |
| 62110 | 2008 | Predicted | 10.0% | 3.1% | 6.8% | 22.7% | 41.3% | 10.1% | 6.0% |
| | | Original | 13.4% | 3.3% | 7.7% | 26.9% | 35.6% | 8.9% | 4.3% |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3
|
niksmer/RoBERTa-RILE
|
niksmer
| 2022-03-24T09:19:40Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
metrics:
- accuracy
- precision
- recall
model-index:
- name: RoBERTa-RILE
results: []
widget:
- text: "Russia must end the war."
- text: "Democratic institutions must be supported."
- text: "The state must fight political corruption."
- text: "Our energy economy must be nationalised."
- text: "We must increase social spending."
---
# RoBERTa-RILE
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/).
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of three political categories: "neutral", "left", "right".
## Intended uses & limitations
The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance.
```python
from transformers import pipeline
import pandas as pd
classifier = pipeline(
task="text-classification",
model="niksmer/RoBERTa-RILE")
# Load text data you want to classify
text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list()
# Inference
output = classifier(text)
# Print output
pd.DataFrame(output).head()
```
## Training and evaluation data
## Training and evaluation data
RoBERTa-RILE was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020.
| Country | Count manifestos | Count sentences | Time span |
|----------------|------------------|-----------------|--------------------|
| Australia | 18 | 14,887 | 2010-2016 |
| Ireland | 23 | 24,966 | 2007-2016 |
| Canada | 14 | 12,344 | 2004-2008 & 2015 |
| New Zealand | 46 | 35,079 | 1993-2017 |
| South Africa | 29 | 13,334 | 1994-2019 |
| USA | 9 | 13,188 | 1992 & 2004-2020 |
| United Kingdom | 34 | 30,936 | 1997-2019 |
Canadian manifestos between 2004 and 2008 are used as test data.
The Manifesto Project mannually annotates individual sentences from political party manifestos in over 50 main categories - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each categorie. It has created a valid left-right-scale, the rile-index, to aaggregate manifesto in a standardized, onde-dimensional political space from left to right based on saliency-theory.
RoBERTa-RILE classifies texts based on the rile index.
### Tain data
Train data was slightly imbalanced.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 52,277 |
| 1 | left | 37,106 |
| 2 | right | 26,560 |
Overall count: 115,943
### Validation data
The validation was created by chance.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 9,198 |
| 1 | left | 6,637 |
| 2 | right | 4,626 |
Overall count: 20,461
### Test data
The test dataset contains ten canadian manifestos between 2004 and 2008.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 3,881 |
| 1 | left | 2,611 |
| 2 | right | 1,838 |
Overall count: 8,330
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
```
training_args = TrainingArguments(
warmup_ratio=0.05,
weight_decay=0.1,
learning_rate=1e-05,
fp16 = True,
evaluation_strategy="epoch",
num_train_epochs=5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
save_strategy="no",
logging_dir='logs',
logging_strategy= 'steps',
logging_steps=10,
push_to_hub=True,
hub_strategy="end")
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 0.7442 | 1.0 | 1812 | 0.6827 | 0.7120 | 0.7120 | 0.7007 | 0.7126 | 0.7120 | 0.7120 |
| 0.6447 | 2.0 | 3624 | 0.6618 | 0.7281 | 0.7281 | 0.7169 | 0.7281 | 0.7281 | 0.7281 |
| 0.5467 | 3.0 | 5436 | 0.6657 | 0.7309 | 0.7309 | 0.7176 | 0.7295 | 0.7309 | 0.7309 |
| 0.5179 | 4.0 | 7248 | 0.6654 | 0.7346 | 0.7346 | 0.7240 | 0.7345 | 0.7346 | 0.7346 |
| 0.4787 | 5.0 | 9060 | 0.6757 | 0.7350 | 0.7350 | 0.7241 | 0.7347 | 0.7350 | 0.7350 |
### Validation evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| RoBERTa-RILE | 0.74 | 0.72 | 0.73 |
### Test evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| RoBERTa-RILE | 0.69 | 0.67 | 0.69 |
### Evaluation per category
| Label | Validation F1-Score | Test F1-Score |
|-----------------------------|---------------------|---------------|
| neutral | 0.77 | 0.74 |
| left | 0.73 | 0.65 |
| right | 0.67 | 0.62 |
### Evaluation based on saliency theory
Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent.
Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology.
The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions).
In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use [ManiBERT](https://huggingface.co/niksmer/ManiBERT).
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3
|
niksmer/ManiBERT
|
niksmer
| 2022-03-24T09:03:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
metrics:
- accuracy
- precision
- recall
model-index:
- name: ManiBERT
results: []
widget:
- text: "Russia must end the war."
- text: "Democratic institutions must be supported."
- text: "The state must fight political corruption."
- text: "Our energy economy must be nationalised."
- text: "We must increase social spending."
---
# ManiBERT
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/).
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of 56 political categories:
## Intended uses & limitations
The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance.
```python
from transformers import pipeline
import pandas as pd
classifier = pipeline(
task="text-classification",
model="niksmer/ManiBERT")
# Load text data you want to classify
text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list()
# Inference
output = classifier(text)
# Print output
pd.DataFrame(output).head()
```
## Train Data
ManiBERT was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020.
| Country | Count manifestos | Count sentences | Time span |
|----------------|------------------|-----------------|--------------------|
| Australia | 18 | 14,887 | 2010-2016 |
| Ireland | 23 | 24,966 | 2007-2016 |
| Canada | 14 | 12,344 | 2004-2008 & 2015 |
| New Zealand | 46 | 35,079 | 1993-2017 |
| South Africa | 29 | 13,334 | 1994-2019 |
| USA | 9 | 13,188 | 1992 & 2004-2020 |
| United Kingdom | 34 | 30,936 | 1997-2019 |
Canadian manifestos between 2004 and 2008 are used as test data.
The resulting Datasets are higly (!) imbalanced. See Evaluation.
## Evaluation
| Description | Label | Count Train Data | Count Validation Data | Count Test Data | Validation F1-Score | Test F1-Score |
|-------------------------------------------------------------------|-------|------------------|-----------------------|-----------------|---------------------|---------------|
| Foreign Special Relationships: Positive | 0 | 545 | 96 | 60 | 0.43 | 0.45 |
| Foreign Special Relationships: Negative | 1 | 66 | 14 | 22 | 0.22 | 0.09 |
| Anti-Imperialism | 2 | 93 | 16 | 1 | 0.16 | 0.00 |
| Military: Positive | 3 | 1,969 | 356 | 159 | 0.69 | 0.63 |
| Military: Negative | 4 | 489 | 89 | 52 | 0.59 | 0.63 |
| Peace | 5 | 418 | 80 | 49 | 0.57 | 0.64 |
| Internationalism: Positive | 6 | 2,401 | 417 | 404 | 0.60 | 0.54 |
| European Community/Union or Latin America Integration: Positive | 7 | 930 | 156 | 20 | 0.58 | 0.32 |
| Internationalism: Negative | 8 | 209 | 40 | 57 | 0.28 | 0.05 |
| European Community/Union or Latin America Integration: Negative | 9 | 520 | 81 | 0 | 0.39 | - |
| Freedom and Human Rights | 10 | 2,196 | 389 | 76 | 0.50 | 0.34 |
| Democracy | 11 | 3,045 | 534 | 206 | 0.53 | 0.51 |
| Constitutionalism: Positive | 12 | 259 | 48 | 12 | 0.34 | 0.22 |
| Constitutionalism: Negative | 13 | 380 | 72 | 2 | 0.34 | 0.00 |
| Decentralisation: Positive | 14 | 2,791 | 481 | 331 | 0.49 | 0.45 |
| Centralisation: Positive | 15 | 150 | 33 | 71 | 0.11 | 0.00 |
| Governmental and Administrative Efficiency | 16 | 3,905 | 711 | 105 | 0.50 | 0.32 |
| Political Corruption | 17 | 900 | 186 | 234 | 0.59 | 0.55 |
| Political Authority | 18 | 3,488 | 627 | 300 | 0.51 | 0.39 |
| Free Market Economy | 19 | 1,768 | 309 | 53 | 0.40 | 0.16 |
| Incentives: Positive | 20 | 3,100 | 544 | 81 | 0.52 | 0.28 |
| Market Regulation | 21 | 3,562 | 616 | 210 | 0.50 | 0.36 |
| Economic Planning | 22 | 533 | 93 | 67 | 0.31 | 0.12 |
| Corporatism/ Mixed Economy | 23 | 193 | 32 | 23 | 0.28 | 0.33 |
| Protectionism: Positive | 24 | 633 | 103 | 180 | 0.44 | 0.22 |
| Protectionism: Negative | 25 | 723 | 118 | 149 | 0.52 | 0.40 |
| Economic Goals | 26 | 817 | 139 | 148 | 0.05 | 0.00 |
| Keynesian Demand Management | 27 | 160 | 25 | 9 | 0.00 | 0.00 |
| Economic Growth: Positive | 28 | 3,142 | 607 | 374 | 0.53 | 0.30 |
| Technology and Infrastructure: Positive | 29 | 8,643 | 1,529 | 339 | 0.71 | 0.56 |
| Controlled Economy | 30 | 567 | 96 | 94 | 0.47 | 0.16 |
| Nationalisation | 31 | 832 | 157 | 27 | 0.56 | 0.16 |
| Economic Orthodoxy | 32 | 1,721 | 287 | 184 | 0.55 | 0.48 |
| Marxist Analysis: Positive | 33 | 148 | 33 | 0 | 0.20 | - |
| Anti-Growth Economy and Sustainability | 34 | 2,676 | 452 | 250 | 0.43 | 0.33 |
| Environmental Protection | 35 | 6,731 | 1,163 | 934 | 0.70 | 0.67 |
| Culture: Positive | 36 | 2,082 | 358 | 92 | 0.69 | 0.56 |
| Equality: Positive | 37 | 6,630 | 1,126 | 361 | 0.57 | 0.43 |
| Welfare State Expansion | 38 | 13,486 | 2,405 | 990 | 0.72 | 0.61 |
| Welfare State Limitation | 39 | 926 | 151 | 2 | 0.45 | 0.00 |
| Education Expansion | 40 | 7,191 | 1,324 | 274 | 0.78 | 0.63 |
| Education Limitation | 41 | 154 | 27 | 1 | 0.17 | 0.00 |
| National Way of Life: Positive | 42 | 2,105 | 385 | 395 | 0.48 | 0.34 |
| National Way of Life: Negative | 43 | 743 | 147 | 2 | 0.27 | 0.00 |
| Traditional Morality: Positive | 44 | 1,375 | 234 | 19 | 0.55 | 0.14 |
| Traditional Morality: Negative | 45 | 291 | 54 | 38 | 0.30 | 0.23 |
| Law and Order | 46 | 5,582 | 949 | 381 | 0.72 | 0.71 |
| Civic Mindedness: Positive | 47 | 1,348 | 229 | 27 | 0.45 | 0.28 |
| Multiculturalism: Positive | 48 | 2,006 | 355 | 71 | 0.61 | 0.35 |
| Multiculturalism: Negative | 49 | 144 | 31 | 7 | 0.33 | 0.00 |
| Labour Groups: Positive | 50 | 3,856 | 707 | 57 | 0.64 | 0.14 |
| Labour Groups: Negative | 51 | 208 | 35 | 0 | 0.44 | - |
| Agriculture and Farmers | 52 | 2,996 | 490 | 130 | 0.67 | 0.56 |
| Middle Class and Professional Groups | 53 | 271 | 38 | 12 | 0.38 | 0.40 |
| Underprivileged Minority Groups | 54 | 1,417 | 252 | 82 | 0.34 | 0.33 |
| Non-economic Demographic Groups | 55 | 2,429 | 435 | 106 | 0.42 | 0.24 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
```
training_args = TrainingArguments(
warmup_ratio=0.05,
weight_decay=0.1,
learning_rate=5e-05,
fp16 = True,
evaluation_strategy="epoch",
num_train_epochs=5,
per_device_train_batch_size=16,
overwrite_output_dir=True,
per_device_eval_batch_size=16,
save_strategy="no",
logging_dir='logs',
logging_strategy= 'steps',
logging_steps=10,
push_to_hub=True,
hub_strategy="end")
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 1.7638 | 1.0 | 1812 | 1.6471 | 0.5531 | 0.5531 | 0.3354 | 0.5368 | 0.5531 | 0.5531 |
| 1.4501 | 2.0 | 3624 | 1.5167 | 0.5807 | 0.5807 | 0.3921 | 0.5655 | 0.5807 | 0.5807 |
| 1.0638 | 3.0 | 5436 | 1.5017 | 0.5893 | 0.5893 | 0.4240 | 0.5789 | 0.5893 | 0.5893 |
| 0.9263 | 4.0 | 7248 | 1.5173 | 0.5975 | 0.5975 | 0.4499 | 0.5901 | 0.5975 | 0.5975 |
| 0.7859 | 5.0 | 9060 | 1.5574 | 0.5978 | 0.5978 | 0.4564 | 0.5903 | 0.5978 | 0.5978 |
### Overall evaluation
| Type | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| Validation | 0.60 | 0.46 | 0.59 |
| Test | 0.48 | 0.30 | 0.47 |
### Evaluation based on saliency theory
Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent.
Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology.
The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions).
In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE).
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3
|
huggingtweets/tariqnasheed
|
huggingtweets
| 2022-03-24T08:54:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T08:47:22Z |
---
language: en
thumbnail: http://www.huggingtweets.com/tariqnasheed/1648112086220/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506809010988539910/bBCRvJ4K_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Tariq Nasheed 🇺🇸</div>
<div style="text-align: center; font-size: 14px;">@tariqnasheed</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Tariq Nasheed 🇺🇸.
| Data | Tariq Nasheed 🇺🇸 |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 273 |
| Short tweets | 396 |
| Tweets kept | 2566 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/f1jq7tem/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tariqnasheed's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2dn7iubq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2dn7iubq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/tariqnasheed')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
etomoscow/T5_paraphrase_detector
|
etomoscow
| 2022-03-24T07:52:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-24T07:18:54Z |
---
license: afl-3.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [PAWS](https://github.com/google-research-datasets/paws) for paraphrase generation.
### Details of T5
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the downstream task (Binary Paraphrase Classification)
Dataset: ```PAWS``` [link](https://github.com/google-research-datasets/paws)
## Performance:
F1-score: 0.86
ROC-AUC score: 0.86
## Usage:
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
# use GPU for better performance
device = torch.device('cuda')
tokenizer = T5Tokenizer.from_pretrained("etomoscow/T5_paraphrase_detector")
model = T5ForConditionalGeneration.from_pretrained("etomoscow/T5_paraphrase_detector").to(device)
text_1 = 'During her sophomore , junior and senior summers , she spent half of it with her Alaska team , and half playing , and living in Oregon .'
text_2 = 'During her second , junior and senior summers , she spent half of it with her Alaska team , half playing and living in Oregon.'
true_label = '1'
input_text = tokenizer.encode_plus(text_1 + ' <sep> ' + text_2, return_tensors='pt')
out = model.generate(input_text['input_ids'].to(device))
print(tokenizer.decode(out.squeeze(0), skip_special_tokens=True))
# 1
```
|
tartuNLP/liv4ever-hugging-mt
|
tartuNLP
| 2022-03-24T07:33:01Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"translation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T01:38:25Z |
---
license: apache-2.0
tags:
- translation
widget:
- text: "<2li> Let us generate some Livonian text!"
---
|
nguyenvulebinh/iwslt-asr-wav2vec-large-4500h
|
nguyenvulebinh
| 2022-03-24T07:12:52Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:common_voice",
"dataset:librispeech_asr",
"dataset:how2",
"dataset:must-c-v1",
"dataset:must-c-v2",
"dataset:europarl",
"dataset:tedlium",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-23T14:53:55Z |
---
language: en
datasets:
- common_voice
- librispeech_asr
- how2
- must-c-v1
- must-c-v2
- europarl
- tedlium
tags:
- audio
- automatic-speech-recognition
license: cc-by-nc-4.0
---
# Fine-Tune Wav2Vec2 large model for English ASR
### Data for fine-tune
| Dataset | Duration in hours |
|--------------|-------------------|
| Common Voice | 1667 |
| Europarl | 85 |
| How2 | 356 |
| Librispeech | 936 |
| MuST-C v1 | 407 |
| MuST-C v2 | 482 |
| Tedlium | 482 |
### Evaluation result
| Dataset | Duration in hours | WER w/o LM | WER with LM |
|-------------|-------------------|------------|-------------|
| Librispeech | 5.4 | 2.9 | 1.1 |
| Tedlium | 2.6 | 7.9 | 5.4 |
### Usage
[](https://colab.research.google.com/drive/1FAhtGvjRdHT4W0KeMdMMlL7sm6Hbe7dv?usp=sharing)
```python
from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
from transformers import Wav2Vec2ProcessorWithLM
from IPython.lib.display import Audio
import torchaudio
import torch
# Load model & processor
model_name = "nguyenvulebinh/iwslt-asr-wav2vec-large-4500h"
model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
# Load an example audio (16k)
audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="tst_2010_sample.wav")))
input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')
# Infer
output = model(**input_data)
# Output transcript without LM
print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
# and of course there's teams that have a lot more tada structures and among the best are recent graduates of kindergarten
# Output transcript with LM
print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
# and of course there are teams that have a lot more ta da structures and among the best are recent graduates of kindergarten
```
### Model Parameters License
The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
### Contact
[email protected]
[](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
|
quincyqiang/chinese-roberta-wwm-ext
|
quincyqiang
| 2022-03-24T04:58:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-24T04:52:35Z |
---
license: apache-2.0
---
|
huggingtweets/btohtoh-willitbetoomuch
|
huggingtweets
| 2022-03-24T02:06:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T01:50:00Z |
---
language: en
thumbnail: http://www.huggingtweets.com/btohtoh-willitbetoomuch/1648087519902/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506402743296020484/X79Yfcx5_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1488467916198539265/3pTy_Kr3_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">BToh & unloading</div>
<div style="text-align: center; font-size: 14px;">@btohtoh-willitbetoomuch</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from BToh & unloading.
| Data | BToh | unloading |
| --- | --- | --- |
| Tweets downloaded | 3241 | 85 |
| Retweets | 347 | 0 |
| Short tweets | 480 | 3 |
| Tweets kept | 2414 | 82 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d3flykp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btohtoh-willitbetoomuch's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lp51jew) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lp51jew/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/btohtoh-willitbetoomuch')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
rurupang/roberta-base-finetuned-sts
|
rurupang
| 2022-03-24T01:54:26Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-22T14:13:32Z |
---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- pearsonr
model-index:
- name: roberta-base-finetuned-sts
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: sts
metrics:
- name: Pearsonr
type: pearsonr
value: 0.956039443806831
---
<!-- 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. -->
# roberta-base-finetuned-sts
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1999
- Pearsonr: 0.9560
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearsonr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 329 | 0.2462 | 0.9478 |
| 1.2505 | 2.0 | 658 | 0.1671 | 0.9530 |
| 1.2505 | 3.0 | 987 | 0.1890 | 0.9525 |
| 0.133 | 4.0 | 1316 | 0.2360 | 0.9548 |
| 0.0886 | 5.0 | 1645 | 0.2265 | 0.9528 |
| 0.0886 | 6.0 | 1974 | 0.2097 | 0.9518 |
| 0.0687 | 7.0 | 2303 | 0.2281 | 0.9523 |
| 0.0539 | 8.0 | 2632 | 0.2212 | 0.9542 |
| 0.0539 | 9.0 | 2961 | 0.1843 | 0.9532 |
| 0.045 | 10.0 | 3290 | 0.1999 | 0.9560 |
| 0.0378 | 11.0 | 3619 | 0.2357 | 0.9533 |
| 0.0378 | 12.0 | 3948 | 0.2134 | 0.9541 |
| 0.033 | 13.0 | 4277 | 0.2273 | 0.9540 |
| 0.03 | 14.0 | 4606 | 0.2148 | 0.9533 |
| 0.03 | 15.0 | 4935 | 0.2207 | 0.9534 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/btohtoh
|
huggingtweets
| 2022-03-24T01:35:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T01:35:48Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506402743296020484/X79Yfcx5_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">BToh</div>
<div style="text-align: center; font-size: 14px;">@btohtoh</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from BToh.
| Data | BToh |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 347 |
| Short tweets | 480 |
| Tweets kept | 2414 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xnk5832/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btohtoh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gdcu3k6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gdcu3k6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/btohtoh')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
microsoft/amos
|
microsoft
| 2022-03-24T01:24:38Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T01:16:31Z |
---
license: mit
---
# Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
This model card contains the AMOS model (**base++** version) proposed in [this paper](). The official GitHub repository can be found [here](https://github.com/microsoft/AMOS).
# Citation
If you find this model card useful for your research, please cite the following paper:
```
@inproceedings{meng2022amos,
title={Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators},
author={Meng, Yu and Xiong, Chenyan and Bajaj, Payal and Tiwary, Saurabh and Bennett, Paul and Han, Jiawei and Song, Xia},
booktitle={ICLR},
year={2022}
}
```
|
negfir/distilbert-base-uncased-finetuned-cola
|
negfir
| 2022-03-24T00:39:00Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-15T15:29:20Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: negfir/distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# negfir/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [negfir/uncased_L-12_H-128_A-2](https://huggingface.co/negfir/uncased_L-12_H-128_A-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6077
- Validation Loss: 0.6185
- Train Matthews Correlation: 0.0
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.6116 | 0.6187 | 0.0 | 0 |
| 0.6070 | 0.6190 | 0.0 | 1 |
| 0.6077 | 0.6185 | 0.0 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
espnet/chai_microsoft_indian_langs_te
|
espnet
| 2022-03-24T00:36:45Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"te",
"dataset:microsoft_indian_languages_interspeech2018",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-23T23:36:26Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: te
datasets:
- microsoft_indian_languages_interspeech2018
license: cc-by-4.0
---
## ESPnet2 model
### ``
This model was trained by Chaitanya Narisetty using recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/ms_indic_is18/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/chai_microsoft_indian_langs_te
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue Mar 22 13:38:24 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.8.1+cu111`
- Git hash: `f91410f712d1287cd6809c5bf26b54c5a40fe314`
- Commit date: `Mon Mar 14 22:32:17 2022 -0400`
## asr_train_asr_xlsr53_conformer_raw_te_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|28413|78.0|19.5|2.5|2.4|24.4|80.1|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test_te|3040|28413|78.0|19.4|2.6|2.4|24.4|79.7|
|decode_transformer5_lm_lm_train_lm_transformer_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|28413|78.0|19.5|2.6|2.5|24.5|79.9|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|229419|95.6|2.2|2.2|1.6|6.1|80.1|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test_te|3040|229419|95.6|2.2|2.2|1.6|6.0|79.7|
|decode_transformer5_lm_lm_train_lm_transformer_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|229419|95.6|2.1|2.2|1.6|6.0|79.9|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|146657|92.7|4.7|2.6|1.6|8.9|80.1|
|decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test_te|3040|146657|92.8|4.7|2.6|1.6|8.9|79.7|
|decode_transformer5_lm_lm_train_lm_transformer_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|146657|92.8|4.6|2.6|1.6|8.9|79.9|
## config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_xlsr53_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_xlsr53_conformer_raw_te_bpe150_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 64
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_te_bpe150_sp_ssl/train/speech_shape
- exp/asr_stats_raw_te_bpe150_sp_ssl/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_te_bpe150_sp_ssl/valid/speech_shape
- exp/asr_stats_raw_te_bpe150_sp_ssl/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_te_sp/wav.scp
- speech
- sound
- - dump/raw/train_te_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_te/wav.scp
- speech
- sound
- - dump/raw/dev_te/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0005
scheduler: warmuplr
scheduler_conf:
warmup_steps: 30000
token_list:
- <blank>
- <unk>
- ా
- ు
- ి
- ం
- ే
- వ
- న
- ల
- ▁అ
- క
- ్
- ో
- మ
- ▁
- త
- ర
- ప
- ీ
- ▁మ
- య
- డ
- ▁ప
- ద
- ని
- గ
- ▁వ
- స
- కు
- ె
- ర్
- ▁స
- ▁క
- ్య
- న్న
- ట
- ▁చ
- ▁త
- ాల
- ంట
- ూ
- శ
- ంద
- ార
- ▁న
- ారు
- ▁ఉ
- లు
- ▁ఆ
- ను
- జ
- రి
- ▁ప్ర
- ించ
- ధ
- ై
- హ
- ంది
- ్ర
- ▁ఇ
- చ
- రు
- స్త
- లో
- ▁ద
- డు
- ▁ఎ
- ▁వి
- ల్ల
- ణ
- గా
- ది
- డి
- న్నారు
- దు
- ిన
- ▁ర
- త్
- ొ
- ▁గ
- ంత
- ంగా
- ▁కా
- బ
- ▁జ
- ష
- ▁తెల
- ులు
- ▁ఏ
- ట్ట
- చ్చ
- తి
- నే
- కి
- ంలో
- ▁అవును
- ▁చెప్ప
- భ
- ▁ఈ
- ప్ప
- ▁ని
- ▁రా
- క్క
- ▁బ
- ట్ల
- ▁భ
- తో
- ▁కూడా
- ▁బా
- ద్ద
- ▁చేస
- ▁లే
- ాయి
- ానికి
- త్ర
- ▁కొ
- ఖ
- ▁ఒక
- ▁చాలా
- క్ష
- ళ
- ▁చేస్త
- ృ
- థ
- ఘ
- ఫ
- ఓ
- ౌ
- ఒ
- ఐ
- ఠ
- ఢ
- అ
- ఉ
- ఏ
- ఈ
- ౦
- ఇ
- ః
- ఋ
- ఝ
- ఔ
- ఛ
- ఞ
- ఊ
- ఎ
- ఆ
- ఙ
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: bpe
bpemodel: data/te_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: fused
frontend_conf:
frontends:
- frontend_type: default
n_fft: 512
win_length: 400
hop_length: 160
- frontend_type: s3prl
frontend_conf:
upstream: wav2vec2_xlsr
download_dir: ./hub
multilayer_feature: true
align_method: linear_projection
proj_dim: 200
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 400
output_size: 100
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 15
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
input_layer: embed
num_blocks: 6
linear_units: 2048
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.7a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/russian_commonvoice_blstm
|
espnet
| 2022-03-24T00:02:17Z | 3 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"ru",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-23T23:59:42Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: ru
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/russian_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout fa1b865352475b744c37f70440de1cc6b257ba70
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/russian_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Mar 23 19:56:59 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `fa1b865352475b744c37f70440de1cc6b257ba70`
- Commit date: `Wed Feb 16 16:42:36 2022 -0500`
## asr_blstm_specaug_num_time_mask_2_lr_0.1
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|71189|79.3|18.4|2.4|2.1|22.8|71.1|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|537025|95.0|3.0|2.0|1.1|6.1|71.1|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|399162|93.2|4.5|2.3|1.4|8.2|71.1|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_blstm_specaug_num_time_mask_2_lr_0.1
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_ru_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_ru_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_ru_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_ru_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_ru_sp/wav.scp
- speech
- sound
- - dump/raw/train_ru_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_ru/wav.scp
- speech
- sound
- - dump/raw/dev_ru/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- е
- о
- и
- с
- м
- а
- в
- н
- д
- т
- у
- .
- я
- ы
- л
- й
- з
- п
- к
- но
- ','
- ▁в
- ра
- б
- ж
- ю
- г
- го
- ▁по
- ▁с
- ни
- ч
- х
- р
- ко
- ре
- ш
- ли
- ть
- ▁на
- ль
- ва
- ер
- ▁и
- ет
- ст
- ро
- на
- ла
- ле
- ь
- ен
- то
- ло
- да
- ка
- ▁не
- ств
- ти
- ци
- ся
- ▁за
- ▁про
- че
- ем
- ру
- же
- та
- ▁при
- ▁со
- ▁это
- ри
- ф
- ки
- бо
- ц
- ▁С
- ста
- ения
- щ
- сти
- э
- К
- О
- А
- И
- '-'
- Т
- Я
- Б
- Д
- М
- '?'
- –
- Г
- —
- '!'
- У
- ъ
- '"'
- »
- ё
- Ф
- ':'
- Х
- Ю
- F
- ;
- O
- I
- E
- R
- −
- В
- С
- ''''
- П
- C
- L
- A
- ‐
- H
- T
- G
- S
- (
- )
- B
- K
- P
- Z
- M
- Й
- X
- Ц
- Ж
- Ч
- Ш
- «
- З
- Л
- Е
- Р
- Э
- N
- Н
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/ru_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_ru_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
ydshieh/roberta-base-squad2
|
ydshieh
| 2022-03-23T22:39:25Z | 57 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-23T22:29:51Z |
---
language: en
datasets:
- squad_v2
license: cc-by-4.0
---
# roberta-base for QA
NOTE: This is version 2 of the model. See [this github issue](https://github.com/deepset-ai/FARM/issues/552) from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify `revision="v1.0"` when loading the model in Transformers 3.5. For exmaple:
```
model_name = "deepset/roberta-base-squad2"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
```
## Overview
**Language model:** roberta-base
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py)
**Infrastructure**: 4x Tesla v100
## Hyperparameters
```
batch_size = 96
n_epochs = 2
base_LM_model = "roberta-base"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64
```
## Using a distilled model instead
Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model.
## Performance
Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
```
"exact": 79.87029394424324,
"f1": 82.91251169582613,
"total": 11873,
"HasAns_exact": 77.93522267206478,
"HasAns_f1": 84.02838248389763,
"HasAns_total": 5928,
"NoAns_exact": 81.79983179142137,
"NoAns_f1": 81.79983179142137,
"NoAns_total": 5945
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/roberta-base-squad2"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Why is model conversion important?',
'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
### In FARM
```python
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.tokenization import Tokenizer
from farm.infer import Inferencer
model_name = "deepset/roberta-base-squad2"
# a) Get predictions
nlp = Inferencer.load(model_name, task_type="question_answering")
QA_input = [{"questions": ["Why is model conversion important?"],
"text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)
# b) Load model & tokenizer
model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
tokenizer = Tokenizer.load(model_name)
```
### In haystack
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
# or
reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
```
## Authors
Branden Chan: `branden.chan [at] deepset.ai`
Timo Möller: `timo.moeller [at] deepset.ai`
Malte Pietsch: `malte.pietsch [at] deepset.ai`
Tanay Soni: `tanay.soni [at] deepset.ai`
## About us
We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.
Some of our work:
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
- [FARM](https://github.com/deepset-ai/FARM)
- [Haystack](https://github.com/deepset-ai/haystack/)
Get in touch:
[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
By the way: [we're hiring!](http://www.deepset.ai/jobs)
|
ydshieh/roberta-large-ner-english
|
ydshieh
| 2022-03-23T22:24:57Z | 36 | 2 |
transformers
|
[
"transformers",
"tf",
"roberta",
"token-classification",
"en",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-23T22:13:16Z |
---
language: en
datasets:
- conll2003
widget:
- text: "My name is jean-baptiste and I live in montreal"
- text: "My name is clara and I live in berkeley, california."
- text: "My name is wolfgang and I live in berlin"
---
# roberta-large-ner-english: model fine-tuned from roberta-large for NER task
## Introduction
[roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset.
Model was validated on emails/chat data and outperformed other models on this type of data specifically.
In particular the model seems to work better on entity that don't start with an upper case.
## Training data
Training data was classified as follow:
Abbreviation|Description
-|-
O |Outside of a named entity
MISC |Miscellaneous entity
PER |Person’s name
ORG |Organization
LOC |Location
In order to simplify, the prefix B- or I- from original conll2003 was removed.
I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size:
Train | Validation
-|-
17494 | 3250
## How to use camembert-ner with HuggingFace
##### Load camembert-ner and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
##### Process text sample (from wikipedia)
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne to develop and sell Wozniak's Apple I personal computer")
[{'entity_group': 'ORG',
'score': 0.99381506,
'word': ' Apple',
'start': 0,
'end': 5},
{'entity_group': 'PER',
'score': 0.99970853,
'word': ' Steve Jobs',
'start': 29,
'end': 39},
{'entity_group': 'PER',
'score': 0.99981767,
'word': ' Steve Wozniak',
'start': 41,
'end': 54},
{'entity_group': 'PER',
'score': 0.99956465,
'word': ' Ronald Wayne',
'start': 59,
'end': 71},
{'entity_group': 'PER',
'score': 0.9997918,
'word': ' Wozniak',
'start': 92,
'end': 99},
{'entity_group': 'MISC',
'score': 0.99956393,
'word': ' Apple I',
'start': 102,
'end': 109}]
```
## Model performances
Model performances computed on conll2003 validation dataset (computed on the tokens predictions)
entity|precision|recall|f1
-|-|-|-
PER|0.9914|0.9927|0.9920
ORG|0.9627|0.9661|0.9644
LOC|0.9795|0.9862|0.9828
MISC|0.9292|0.9262|0.9277
Overall|0.9740|0.9766|0.9753
On private dataset (email, chat, informal discussion), computed on word predictions:
entity|precision|recall|f1
-|-|-|-
PER|0.8823|0.9116|0.8967
ORG|0.7694|0.7292|0.7487
LOC|0.8619|0.7768|0.8171
By comparison on the same private dataset, Spacy (en_core_web_trf-3.2.0) was giving:
entity|precision|recall|f1
-|-|-|-
PER|0.9146|0.8287|0.8695
ORG|0.7655|0.6437|0.6993
LOC|0.8727|0.6180|0.7236
|
bigmorning/my-gpt-model-5
|
bigmorning
| 2022-03-23T22:11:47Z | 5 | 1 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T22:04:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-gpt-model-5
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-gpt-model-5
This model is a fine-tuned version of [bigmorning/my-gpt-model-3](https://huggingface.co/bigmorning/my-gpt-model-3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.9979
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 4.9979 | 0 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
BigSalmon/InformalToFormalLincoln30
|
BigSalmon
| 2022-03-23T20:51:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T20:36:45Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln30")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln30")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
|
pere/test-t5-small
|
pere
| 2022-03-23T20:39:40Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-20T12:17:29Z |
---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
---
## Test T5 small conversion
This is a test repo for the conversion of T5X to HuggingFace Flax.
The current model is first converted from MTF to T5X using the conversion script included in the T5X library:
```bash
python3 -m t5x.scripts.convert_tf_checkpoint --gin_file=t5x/examples/t5/t5_1_0/small.gin --gin.convert_checkpoint.model=%MODEL --gin.convert_checkpoint.tf_checkpoint_path=\"gs://t5-data/pretrained_models/small/model.ckpt-1000000\" --gin.convert_checkpoint.output_dir=\"/tmp/t5x_checkpoints/t5_small\" --logtostderr
```
After creating the T5X model, the model is converted to Huggingface Flax by a modified version of the script from @stefan-it (https://gist.githubusercontent.com/stefan-it/30e4998ef159f33696e377a46f699d9f/raw/c19da5d067dc9d31d0b8115a79e8626186e11daa/convert_t5x_checkpoint_to_flax.py). The modified version is included in this repo. The modification is basically that the wi_0 and wi_1 layers are combined into wi. This might be a difference between t5_1_0 and t5_1_1
```bash
python3 convert_t5_checkpoint_to_flax.py --t5x_checkpoint_path /tmp/t5x_checkpoints/t5_small/checkpoint_1000000/ --flax_dump_folder_path /tmp/flax_dump_folder/ --config_name t5-small
```
The tokenizer.json was copied from https://huggingface.co/t5-small/blob/main/tokenizer.json.
To be able to use the widgets in HuggingFace, the model was converted to pyTorch by running:
```python
from transformers import T5ForConditionalGeneration
model =
T5ForConditionalGeneration.from_pretrained(".", from_flax=True)
model.save_pretrained(".")
```
|
bigmorning/my-gpt-model-4
|
bigmorning
| 2022-03-23T20:00:04Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T19:52:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-gpt-model-4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-gpt-model-4
This model is a fine-tuned version of [bigmorning/my-gpt-model-3](https://huggingface.co/bigmorning/my-gpt-model-3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.0556
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 5.0556 | 0 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/ryiacy
|
huggingtweets
| 2022-03-23T19:51:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T19:28:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/ryiacy/1648065062687/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1424813722011410434/73S-oYNT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">cyriac</div>
<div style="text-align: center; font-size: 14px;">@ryiacy</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from cyriac.
| Data | cyriac |
| --- | --- |
| Tweets downloaded | 1050 |
| Retweets | 32 |
| Short tweets | 60 |
| Tweets kept | 958 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26de85bt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ryiacy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2p7goxic) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2p7goxic/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ryiacy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
negfir/uncased_L-12_H-128_A-2
|
negfir
| 2022-03-23T19:18:33Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T18:49:57Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: uncased_L-12_H-128_A-2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uncased_L-12_H-128_A-2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
gayanin/bart-med-term-conditional-masking
|
gayanin
| 2022-03-23T19:06:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-23T14:24:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-med-term-conditional-masking
results: []
---
<!-- 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. -->
# bart-med-term-conditional-masking
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5115
- Rouge2 Precision: 0.7409
- Rouge2 Recall: 0.5343
- Rouge2 Fmeasure: 0.6025
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6278 | 1.0 | 15827 | 0.5546 | 0.7255 | 0.5244 | 0.5908 |
| 0.5356 | 2.0 | 31654 | 0.5286 | 0.7333 | 0.5293 | 0.5966 |
| 0.4757 | 3.0 | 47481 | 0.5154 | 0.7376 | 0.532 | 0.5998 |
| 0.4337 | 4.0 | 63308 | 0.5107 | 0.7406 | 0.5342 | 0.6023 |
| 0.4045 | 5.0 | 79135 | 0.5115 | 0.7409 | 0.5343 | 0.6025 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11
|
DrishtiSharma
| 2022-03-23T18:35:27Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- rm-sursilv
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-rm-sursilv-d11
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 8
args: rm-sursilv
metrics:
- type: wer
value: 0.24094169578811844
name: Test WER
- name: Test CER
type: cer
value: 0.049832791672554284
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: rm-sursilv
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - RM-SURSILV dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2511
- Wer: 0.2415
#### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11 --dataset mozilla-foundation/common_voice_8_0 --config rm-sursilv --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Romansh-Sursilv language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 125.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 2.3958 | 17.44 | 1500 | 0.6808 | 0.6521 |
| 0.9663 | 34.88 | 3000 | 0.3023 | 0.3718 |
| 0.7963 | 52.33 | 4500 | 0.2588 | 0.3046 |
| 0.6893 | 69.77 | 6000 | 0.2436 | 0.2718 |
| 0.6148 | 87.21 | 7500 | 0.2521 | 0.2572 |
| 0.5556 | 104.65 | 9000 | 0.2490 | 0.2442 |
| 0.5258 | 122.09 | 10500 | 0.2515 | 0.2442 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2
|
DrishtiSharma
| 2022-03-23T18:35:22Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- sl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- sl
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-sl-with-LM-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sl
metrics:
- name: Test WER
type: wer
value: 0.21695212999560826
- name: Test CER
type: cer
value: 0.052850080572474256
- name: Test WER (+LM)
type: wer
value: 0.14551310203484116
- name: Test CER (+LM)
type: cer
value: 0.03927566711277415
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Dev WER
type: wer
value: 0.560722380639029
- name: Dev CER
type: cer
value: 0.2279626093074681
- name: Dev WER (+LM)
type: wer
value: 0.46486802661402354
- name: Dev CER (+LM)
type: cer
value: 0.21105136194592422
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 46.69
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2855
- Wer: 0.2401
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9294 | 6.1 | 500 | 2.9712 | 1.0 |
| 2.8305 | 12.2 | 1000 | 1.7073 | 0.9479 |
| 1.4795 | 18.29 | 1500 | 0.5756 | 0.6397 |
| 1.3433 | 24.39 | 2000 | 0.4968 | 0.5424 |
| 1.1766 | 30.49 | 2500 | 0.4185 | 0.4743 |
| 1.0017 | 36.59 | 3000 | 0.3303 | 0.3578 |
| 0.9358 | 42.68 | 3500 | 0.3003 | 0.3051 |
| 0.8358 | 48.78 | 4000 | 0.3045 | 0.2884 |
| 0.7647 | 54.88 | 4500 | 0.2866 | 0.2677 |
| 0.7482 | 60.98 | 5000 | 0.2829 | 0.2585 |
| 0.6943 | 67.07 | 5500 | 0.2782 | 0.2478 |
| 0.6586 | 73.17 | 6000 | 0.2911 | 0.2537 |
| 0.6425 | 79.27 | 6500 | 0.2817 | 0.2462 |
| 0.6067 | 85.37 | 7000 | 0.2910 | 0.2436 |
| 0.5974 | 91.46 | 7500 | 0.2875 | 0.2430 |
| 0.5812 | 97.56 | 8000 | 0.2852 | 0.2396 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
Akashpb13/Hausa_xlsr
|
Akashpb13
| 2022-03-23T18:35:09Z | 53 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"ha",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ha
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- ha
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/Hausa_xlsr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ha
metrics:
- name: Test WER
type: wer
value: 0.20614541257934219
- name: Test CER
type: cer
value: 0.04358048053214061
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ha
metrics:
- name: Test WER
type: wer
value: 0.20614541257934219
- name: Test CER
type: cer
value: 0.04358048053214061
---
# Akashpb13/Hausa_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):
- Loss: 0.275118
- Wer: 0.329955
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Hausa train.tsv, dev.tsv, invalidated.tsv, reported.tsv and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0
## Training procedure
For creating the training dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000096
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|------|---------------|-----------------|----------|
| 500 | 5.175900 | 2.750914 | 1.000000 |
| 1000 | 1.028700 | 0.338649 | 0.497999 |
| 1500 | 0.332200 | 0.246896 | 0.402241 |
| 2000 | 0.227300 | 0.239640 | 0.395839 |
| 2500 | 0.175000 | 0.239577 | 0.373966 |
| 3000 | 0.140400 | 0.243272 | 0.356095 |
| 3500 | 0.119200 | 0.263761 | 0.365164 |
| 4000 | 0.099300 | 0.265954 | 0.353428 |
| 4500 | 0.084400 | 0.276367 | 0.349693 |
| 5000 | 0.073700 | 0.282631 | 0.343825 |
| 5500 | 0.068000 | 0.282344 | 0.341158 |
| 6000 | 0.064500 | 0.281591 | 0.342491 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Akashpb13/Hausa_xlsr --dataset mozilla-foundation/common_voice_8_0 --config ha --split test
```
|
sammy786/wav2vec2-xlsr-bashkir
|
sammy786
| 2022-03-23T18:35:07Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ba",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ba
license: apache-2.0
tags:
- automatic-speech-recognition
- ba
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-bashkir
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ba
metrics:
- name: Test WER
type: wer
value: 11.32
- name: Test CER
type: cer
value: 2.34
---
# sammy786/wav2vec2-xlsr-bashkir
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ba dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss:
- Wer:
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:----:|:-------------:|:---------------:|:--------:|
| 200 | 5.387100 | 1.982867 | 1.000000 |
| 400 | 1.269800 | 0.369958 | 0.545755 |
| 600 | 0.903600 | 0.287705 | 0.465594 |
| 800 | 0.787300 | 0.235142 | 0.417091 |
| 1000 | 0.816300 | 0.206325 | 0.390534 |
| 1200 | 0.700500 | 0.197106 | 0.383987 |
| 1400 | 0.707100 | 0.179855 | 0.381368 |
| 1600 | 0.657800 | 0.181605 | 0.370593 |
| 1800 | 0.647800 | 0.168626 | 0.358767 |
| 2000 | 0.650700 | 0.164833 | 0.351483 |
| 2200 | 0.490900 | 0.168133 | 0.363309 |
| 2400 | 0.431000 | 0.161201 | 0.344350 |
| 2600 | 0.372100 | 0.160254 | 0.338280 |
| 2800 | 0.367500 | 0.150885 | 0.329687 |
| 3000 | 0.351300 | 0.154112 | 0.331392 |
| 3200 | 0.314800 | 0.147147 | 0.326700 |
| 3400 | 0.316800 | 0.142681 | 0.325090 |
| 3600 | 0.313000 | 0.138736 | 0.319553 |
| 3800 | 0.291800 | 0.138166 | 0.315570 |
| 4000 | 0.311300 | 0.135977 | 0.322894 |
| 4200 | 0.304900 | 0.128820 | 0.308627 |
| 4400 | 0.301600 | 0.129475 | 0.307440 |
| 4600 | 0.281800 | 0.131863 | 0.305967 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-bashkir --dataset mozilla-foundation/common_voice_8_0 --config ba --split test
```
|
nouamanetazi/wav2vec2-xls-r-300m-ar
|
nouamanetazi
| 2022-03-23T18:35:04Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ar
license: apache-2.0
tags:
- ar
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: XLS-R-300M - Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 1.0
- name: Test CER
type: cer
value: 1.0
---
<!-- 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-ar
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 - AR dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.0191
- eval_wer: 1.0
- eval_runtime: 252.2389
- eval_samples_per_second: 30.217
- eval_steps_per_second: 0.476
- epoch: 1.0
- step: 340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
Please use the evaluation script `eval.py` included in the repo.
1. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id nouamanetazi/wav2vec2-xls-r-300m-ar --dataset speech-recognition-community-v2/dev_data --config ar --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
|
masapasa/xls-r-300m-it-cv8-ds13
|
masapasa
| 2022-03-23T18:35:02Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"it",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- it
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
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: it
metrics:
- name: Test WER
type: wer
value: 100.0
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: it
metrics:
- name: Test WER
type: wer
value: 100.0
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: it
metrics:
- name: Test WER
type: wer
value: 100.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3549
- Wer: 0.3827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4129 | 5.49 | 500 | 3.3224 | 1.0 |
| 2.9323 | 10.98 | 1000 | 2.9128 | 1.0000 |
| 1.6839 | 16.48 | 1500 | 0.7740 | 0.6854 |
| 1.485 | 21.97 | 2000 | 0.5830 | 0.5976 |
| 1.362 | 27.47 | 2500 | 0.4866 | 0.4905 |
| 1.2752 | 32.96 | 3000 | 0.4240 | 0.4967 |
| 1.1957 | 38.46 | 3500 | 0.3899 | 0.4258 |
| 1.1646 | 43.95 | 4000 | 0.3597 | 0.4014 |
| 1.1265 | 49.45 | 4500 | 0.3559 | 0.3829 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
manifoldix/xlsr-sg-lm
|
manifoldix
| 2022-03-23T18:34:59Z | 9 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"gsw",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: gsw
tags:
- hf-asr-leaderboard
- robust-speech-event
widget:
- example_title: swiss parliament sample 1
src: https://huggingface.co/manifoldix/xlsr-sg-lm/resolve/main/07e73bcaa2ab192aea9524d72db45f34f274d1b3d5672434c462d32d44d792be.mp3
- example_title: swiss parliament sample 2
src: https://huggingface.co/manifoldix/xlsr-sg-lm/resolve/main/14a2f855363920f111c7b30e8632c19e5f340ab5031e1ed2621db39baf452ae0.mp3
model-index:
- name: XLS-R-1b Wav2Vec2 Swiss German
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test WER on Swiss parliament
type: wer
value: 34.6%
- name: Test WER on Swiss dialect test set
type: wer
value: 40%
---
## XLSR-1b Swiss German
Fine-tuned on the Swiss parliament dataset from FHNW v1 (70h).
Tested on the Swiss parliament test set with a WER of 34.6%
Tested on the "Swiss German Dialects" with a WER of 40%
Both test sets can be accessed here: [fhnw_datasets](https://www.cs.technik.fhnw.ch/i4ds-datasets)
The Swiss German dialect private test set has been uploaded on huggingface: [huggingface_swiss_dialects](https://huggingface.co/datasets/manifoldix/swg_parliament_fhnw)
|
infinitejoy/wav2vec2-large-xls-r-300m-hungarian
|
infinitejoy
| 2022-03-23T18:34:54Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hu",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hu
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hu
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Hungarian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: hu
metrics:
- name: Test WER
type: wer
value: 31.099
- name: Test CER
type: cer
value: 6.737
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hu
metrics:
- name: Test WER
type: wer
value: 45.469
- name: Test CER
type: cer
value: 15.727
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: hu
metrics:
- name: Test WER
type: wer
value: 48.2
---
<!-- 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-hungarian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HU dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2562
- Wer: 0.3112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.3964 | 3.52 | 1000 | 1.2251 | 0.8781 |
| 1.3176 | 7.04 | 2000 | 0.3872 | 0.4462 |
| 1.1999 | 10.56 | 3000 | 0.3244 | 0.3922 |
| 1.1633 | 14.08 | 4000 | 0.3014 | 0.3704 |
| 1.1132 | 17.61 | 5000 | 0.2913 | 0.3623 |
| 1.0888 | 21.13 | 6000 | 0.2864 | 0.3498 |
| 1.0487 | 24.65 | 7000 | 0.2821 | 0.3435 |
| 1.0431 | 28.17 | 8000 | 0.2739 | 0.3308 |
| 0.9896 | 31.69 | 9000 | 0.2629 | 0.3243 |
| 0.9839 | 35.21 | 10000 | 0.2806 | 0.3308 |
| 0.9586 | 38.73 | 11000 | 0.2650 | 0.3235 |
| 0.9501 | 42.25 | 12000 | 0.2585 | 0.3173 |
| 0.938 | 45.77 | 13000 | 0.2561 | 0.3117 |
| 0.921 | 49.3 | 14000 | 0.2559 | 0.3115 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-galician
|
infinitejoy
| 2022-03-23T18:34:49Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"gl",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- gl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- gl
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Galician
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: gl
metrics:
- name: Test WER
type: wer
value: 101.54
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: gl
metrics:
- name: Test WER
type: wer
value: 105.69
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: gl
metrics:
- name: Test WER
type: wer
value: 101.95
---
<!-- 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-galician
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1525
- Wer: 0.1542
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0067 | 4.35 | 500 | 2.9632 | 1.0 |
| 1.4939 | 8.7 | 1000 | 0.5005 | 0.4157 |
| 0.9982 | 13.04 | 1500 | 0.1967 | 0.1857 |
| 0.8726 | 17.39 | 2000 | 0.1587 | 0.1564 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
emre/wav2vec2-xls-r-300m-gl-CV8
|
emre
| 2022-03-23T18:34:43Z | 5 | 0 |
transformers
|
[
"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"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: gl
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-gl-CV8
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice gl
type: common_voice
args: gl
metrics:
- name: Test WER
type: wer
value: 0.208
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: gl
metrics:
- name: Test WER
type: wer
value: 22.94
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: gl
metrics:
- name: Test WER
type: wer
value: 47.82
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: gl
metrics:
- name: Test WER
type: wer
value: 50.8
---
<!-- 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/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
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9427 | 4.9 | 500 | 2.8801 | 1.0 |
| 2.1594 | 9.8 | 1000 | 0.4092 | 0.4001 |
| 0.7332 | 14.71 | 1500 | 0.2151 | 0.2080 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
emre/wav2vec2-xls-r-300m-ab-CV8
|
emre
| 2022-03-23T18:34:41Z | 7 | 0 |
transformers
|
[
"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"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: ab
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-ab-CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ab
metrics:
- name: Test WER
type: wer
value: 44.9
---
# 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
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.7729 | 0.63 | 500 | 3.0624 | 1.0021 |
| 2.7348 | 1.26 | 1000 | 1.0460 | 0.9815 |
| 1.2756 | 1.9 | 1500 | 0.4618 | 0.8309 |
| 1.0419 | 2.53 | 2000 | 0.3725 | 0.7449 |
| 0.9491 | 3.16 | 2500 | 0.3368 | 0.7345 |
| 0.9006 | 3.79 | 3000 | 0.3014 | 0.6936 |
| 0.8519 | 4.42 | 3500 | 0.2852 | 0.6767 |
| 0.8243 | 5.06 | 4000 | 0.2701 | 0.6504 |
| 0.7902 | 5.69 | 4500 | 0.2641 | 0.6221 |
| 0.7767 | 6.32 | 5000 | 0.2549 | 0.6192 |
| 0.7516 | 6.95 | 5500 | 0.2515 | 0.6179 |
| 0.737 | 7.59 | 6000 | 0.2408 | 0.5963 |
| 0.7217 | 8.22 | 6500 | 0.2429 | 0.6261 |
| 0.7101 | 8.85 | 7000 | 0.2366 | 0.5687 |
| 0.6922 | 9.48 | 7500 | 0.2277 | 0.5680 |
| 0.6866 | 10.11 | 8000 | 0.2242 | 0.5847 |
| 0.6703 | 10.75 | 8500 | 0.2222 | 0.5803 |
| 0.6649 | 11.38 | 9000 | 0.2247 | 0.5765 |
| 0.6513 | 12.01 | 9500 | 0.2182 | 0.5644 |
| 0.6369 | 12.64 | 10000 | 0.2128 | 0.5508 |
| 0.6425 | 13.27 | 10500 | 0.2132 | 0.5514 |
| 0.6399 | 13.91 | 11000 | 0.2116 | 0.5495 |
| 0.6208 | 14.54 | 11500 | 0.2105 | 0.5474 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
Baybars/wav2vec2-xls-r-300m-cv8-turkish
|
Baybars
| 2022-03-23T18:34:22Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
- tr
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4164
- Wer: 0.3098
- Cer: 0.0764
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Language Model
N-gram language model is trained by [mpoyraz](https://huggingface.co/mpoyraz/wav2vec2-xls-r-300m-cv7-turkish) on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.6356 | 9.09 | 500 | 0.5055 | 0.5536 | 0.1381 |
| 0.3847 | 18.18 | 1000 | 0.4002 | 0.4247 | 0.1065 |
| 0.3377 | 27.27 | 1500 | 0.4193 | 0.4167 | 0.1078 |
| 0.2175 | 36.36 | 2000 | 0.4351 | 0.3861 | 0.0974 |
| 0.2074 | 45.45 | 2500 | 0.3962 | 0.3622 | 0.0916 |
| 0.159 | 54.55 | 3000 | 0.4062 | 0.3526 | 0.0888 |
| 0.1882 | 63.64 | 3500 | 0.3991 | 0.3445 | 0.0850 |
| 0.1766 | 72.73 | 4000 | 0.4214 | 0.3396 | 0.0847 |
| 0.116 | 81.82 | 4500 | 0.4182 | 0.3265 | 0.0812 |
| 0.0718 | 90.91 | 5000 | 0.4259 | 0.3191 | 0.0781 |
| 0.019 | 100.0 | 5500 | 0.4164 | 0.3098 | 0.0764 |
## Evaluation Commands
Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing.
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset mozilla-foundation/common_voice_8_0 --config tr --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
vutankiet2901/wav2vec2-xls-r-1b-ja
|
vutankiet2901
| 2022-03-23T18:34:17Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"ja",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- ja
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- ja
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-1b
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 11.77
- name: Test CER (with LM)
type: cer
value: 5.22
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 12.23
- name: Test CER (with LM)
type: cer
value: 5.33
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 29.35
- name: Test CER (with LM)
type: cer
value: 16.43
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ja
metrics:
- name: Test CER
type: cer
value: 19.48
---
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA
### Benchmark WER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|
|without LM| 16.97 | 17.95 |
|with 4-grams LM| 11.77 | 12.23|
### Benchmark CER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|
|without LM| 6.82 | 7.05 |
|with 4-grams LM| 5.22 | 5.33 |
## Evaluation
Please use the eval.py file to run the evaluation:
```python
pip install mecab-python3 unidic-lite pykakasi
python eval.py --model_id vutankiet2901/wav2vec2-xls-r-1b-ja --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.484 | 9.49 | 1500 | 1.1849 | 0.7543 | 0.4099 |
| 1.3582 | 18.98 | 3000 | 0.4320 | 0.3489 | 0.1591 |
| 1.1716 | 28.48 | 4500 | 0.3835 | 0.3175 | 0.1454 |
| 1.0951 | 37.97 | 6000 | 0.3732 | 0.3033 | 0.1405 |
| 1.04 | 47.47 | 7500 | 0.3485 | 0.2898 | 0.1360 |
| 0.9768 | 56.96 | 9000 | 0.3386 | 0.2787 | 0.1309 |
| 0.9129 | 66.45 | 10500 | 0.3363 | 0.2711 | 0.1272 |
| 0.8614 | 75.94 | 12000 | 0.3386 | 0.2676 | 0.1260 |
| 0.8092 | 85.44 | 13500 | 0.3356 | 0.2610 | 0.1240 |
| 0.7658 | 94.93 | 15000 | 0.3316 | 0.2564 | 0.1218 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
shahukareem/xls-r-300m-dv
|
shahukareem
| 2022-03-23T18:34:14Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dv",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- dv
license: apache-2.0
tags:
- automatic-speech-recognition
- dv
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Dhivehi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: dv
metrics:
- name: Test WER
type: wer
value: 21.31
- name: Test CER
type: cer
value: 3.82
---
<!-- 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. -->
# xls-r-300m-dv
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: 0.2855
- Wer: 0.2665
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3386 | 0.66 | 400 | 1.1411 | 0.9432 |
| 0.6543 | 1.33 | 800 | 0.5099 | 0.6749 |
| 0.4646 | 1.99 | 1200 | 0.4133 | 0.5968 |
| 0.3748 | 2.65 | 1600 | 0.3534 | 0.5515 |
| 0.3323 | 3.32 | 2000 | 0.3635 | 0.5527 |
| 0.3269 | 3.98 | 2400 | 0.3587 | 0.5423 |
| 0.2984 | 4.64 | 2800 | 0.3340 | 0.5073 |
| 0.2841 | 5.31 | 3200 | 0.3279 | 0.5004 |
| 0.2664 | 5.97 | 3600 | 0.3114 | 0.4845 |
| 0.2397 | 6.63 | 4000 | 0.3174 | 0.4920 |
| 0.2332 | 7.3 | 4400 | 0.3110 | 0.4911 |
| 0.2304 | 7.96 | 4800 | 0.3123 | 0.4785 |
| 0.2134 | 8.62 | 5200 | 0.2984 | 0.4557 |
| 0.2066 | 9.29 | 5600 | 0.3013 | 0.4723 |
| 0.1951 | 9.95 | 6000 | 0.2934 | 0.4487 |
| 0.1806 | 10.61 | 6400 | 0.2802 | 0.4547 |
| 0.1727 | 11.28 | 6800 | 0.2842 | 0.4333 |
| 0.1666 | 11.94 | 7200 | 0.2873 | 0.4272 |
| 0.1562 | 12.6 | 7600 | 0.3042 | 0.4373 |
| 0.1483 | 13.27 | 8000 | 0.3122 | 0.4313 |
| 0.1465 | 13.93 | 8400 | 0.2760 | 0.4226 |
| 0.1335 | 14.59 | 8800 | 0.3112 | 0.4243 |
| 0.1293 | 15.26 | 9200 | 0.3002 | 0.4133 |
| 0.1264 | 15.92 | 9600 | 0.2985 | 0.4145 |
| 0.1179 | 16.58 | 10000 | 0.2925 | 0.4012 |
| 0.1171 | 17.25 | 10400 | 0.3127 | 0.4012 |
| 0.1141 | 17.91 | 10800 | 0.2980 | 0.3908 |
| 0.108 | 18.57 | 11200 | 0.3108 | 0.3951 |
| 0.1045 | 19.24 | 11600 | 0.3269 | 0.3908 |
| 0.1047 | 19.9 | 12000 | 0.2998 | 0.3868 |
| 0.0937 | 20.56 | 12400 | 0.2918 | 0.3875 |
| 0.0949 | 21.23 | 12800 | 0.2906 | 0.3657 |
| 0.0879 | 21.89 | 13200 | 0.2974 | 0.3731 |
| 0.0854 | 22.55 | 13600 | 0.2943 | 0.3711 |
| 0.0851 | 23.22 | 14000 | 0.2919 | 0.3580 |
| 0.0789 | 23.88 | 14400 | 0.2983 | 0.3560 |
| 0.0796 | 24.54 | 14800 | 0.3131 | 0.3544 |
| 0.0761 | 25.21 | 15200 | 0.2996 | 0.3616 |
| 0.0755 | 25.87 | 15600 | 0.2972 | 0.3506 |
| 0.0726 | 26.53 | 16000 | 0.2902 | 0.3474 |
| 0.0707 | 27.2 | 16400 | 0.3083 | 0.3480 |
| 0.0669 | 27.86 | 16800 | 0.3035 | 0.3330 |
| 0.0637 | 28.52 | 17200 | 0.2963 | 0.3370 |
| 0.0596 | 29.19 | 17600 | 0.2830 | 0.3326 |
| 0.0583 | 29.85 | 18000 | 0.2969 | 0.3287 |
| 0.0566 | 30.51 | 18400 | 0.3002 | 0.3480 |
| 0.0574 | 31.18 | 18800 | 0.2916 | 0.3296 |
| 0.0536 | 31.84 | 19200 | 0.2933 | 0.3225 |
| 0.0548 | 32.5 | 19600 | 0.2900 | 0.3179 |
| 0.0506 | 33.17 | 20000 | 0.3073 | 0.3225 |
| 0.0511 | 33.83 | 20400 | 0.2925 | 0.3275 |
| 0.0483 | 34.49 | 20800 | 0.2919 | 0.3245 |
| 0.0456 | 35.16 | 21200 | 0.2859 | 0.3105 |
| 0.0445 | 35.82 | 21600 | 0.2864 | 0.3080 |
| 0.0437 | 36.48 | 22000 | 0.2989 | 0.3084 |
| 0.04 | 37.15 | 22400 | 0.2887 | 0.3060 |
| 0.0406 | 37.81 | 22800 | 0.2870 | 0.3013 |
| 0.0397 | 38.47 | 23200 | 0.2793 | 0.3020 |
| 0.0383 | 39.14 | 23600 | 0.2955 | 0.2943 |
| 0.0345 | 39.8 | 24000 | 0.2813 | 0.2905 |
| 0.0331 | 40.46 | 24400 | 0.2845 | 0.2845 |
| 0.0338 | 41.13 | 24800 | 0.2832 | 0.2925 |
| 0.0333 | 41.79 | 25200 | 0.2889 | 0.2849 |
| 0.0325 | 42.45 | 25600 | 0.2808 | 0.2847 |
| 0.0314 | 43.12 | 26000 | 0.2867 | 0.2801 |
| 0.0288 | 43.78 | 26400 | 0.2865 | 0.2834 |
| 0.0291 | 44.44 | 26800 | 0.2863 | 0.2806 |
| 0.0269 | 45.11 | 27200 | 0.2941 | 0.2736 |
| 0.0275 | 45.77 | 27600 | 0.2897 | 0.2736 |
| 0.0271 | 46.43 | 28000 | 0.2857 | 0.2695 |
| 0.0251 | 47.1 | 28400 | 0.2881 | 0.2702 |
| 0.0243 | 47.76 | 28800 | 0.2901 | 0.2684 |
| 0.0244 | 48.42 | 29200 | 0.2849 | 0.2679 |
| 0.0232 | 49.09 | 29600 | 0.2849 | 0.2677 |
| 0.0224 | 49.75 | 30000 | 0.2855 | 0.2665 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
polodealvarado/xls-r-300m-es
|
polodealvarado
| 2022-03-23T18:34:06Z | 17 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice_8_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"es",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- es
tags:
- common_voice_8_0
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wave2vec-xls-r-300m-es
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_8_0 es
type: mozilla-foundation/common_voice_8_0
args: es
metrics:
- name: Test WER
type: wer
value: 14.6
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: es
metrics:
- name: Test WER
type: wer
value: 28.63
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: es
metrics:
- name: Test WER
type: wer
value: 29.72
---
<!-- 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-XLSR-300m-es
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the spanish common_voice dataset thanks to the GPU credits generously given by the OVHcloud for the Speech Recognition challenge.
It achieves the following results on the evaluation set
Without LM:
- Loss : 0.1900
- Wer : 0.146
With 5-gram:
- WER: 0.109
- CER: 0.036
### Usage with 5-gram.
The model can be used with n-gram (n=5) included in the processor as follows.
```python
import re
from transformers import AutoModelForCTC,Wav2Vec2ProcessorWithLM
import torch
# Loading model and processor
processor = Wav2Vec2ProcessorWithLM.from_pretrained("polodealvarado/xls-r-300m-es")
model = AutoModelForCTC.from_pretrained("polodealvarado/xls-r-300m-es")
# Cleaning characters
def remove_extra_chars(batch):
chars_to_ignore_regex = '[^a-záéíóúñ ]'
text = batch["translation"][target_lang]
batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
return batch
# Preparing dataset
def prepare_dataset(batch):
audio = batch["audio"]
batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"],return_tensors="pt",padding=True).input_values[0]
with processor.as_target_processor():
batch["labels"] = processor(batch["sentence"]).input_ids
return batch
common_voice_test = load_dataset("mozilla-foundation/common_voice_8_0", "es", split="test",use_auth_token=True)
common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])
common_voice_test = common_voice_test.cast_column("audio", Audio(sampling_rate=16_000))
common_voice_test = common_voice_test.map(remove_extra_chars, remove_columns=dataset.column_names)
common_voice_test = common_voice_test.map(prepare_dataset)
# Testing first sample
inputs = torch_tensor(common_voice_test[0]["input_values"])
with torch.no_grad():
logits = model(inputs).logits
pred_ids = torch.argmax(logits, dim=-1)
text = processor.batch_decode(logits.numpy()).text
print(text) # 'bien y qué regalo vas a abrir primero'
```
On the other, you can execute the eval.py file for evaluation
```bash
# To use GPU: --device 0
$ python eval.py --model_id polodealvarado/xls-r-300m-es --dataset mozilla-foundation/common_voice_8_0 --config es --device 0 --split test
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6747 | 0.3 | 400 | 0.6535 | 0.5926 |
| 0.4439 | 0.6 | 800 | 0.3753 | 0.3193 |
| 0.3291 | 0.9 | 1200 | 0.3267 | 0.2721 |
| 0.2644 | 1.2 | 1600 | 0.2816 | 0.2311 |
| 0.24 | 1.5 | 2000 | 0.2647 | 0.2179 |
| 0.2265 | 1.79 | 2400 | 0.2406 | 0.2048 |
| 0.1994 | 2.09 | 2800 | 0.2357 | 0.1869 |
| 0.1613 | 2.39 | 3200 | 0.2242 | 0.1821 |
| 0.1546 | 2.69 | 3600 | 0.2123 | 0.1707 |
| 0.1441 | 2.99 | 4000 | 0.2067 | 0.1619 |
| 0.1138 | 3.29 | 4400 | 0.2044 | 0.1519 |
| 0.1072 | 3.59 | 4800 | 0.1917 | 0.1457 |
| 0.0992 | 3.89 | 5200 | 0.1900 | 0.1438 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-welsh
|
infinitejoy
| 2022-03-23T18:33:58Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"cy",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- cy
license: apache-2.0
tags:
- automatic-speech-recognition
- cy
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Welsh
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: cy
metrics:
- name: Test WER
type: wer
value: 31.003
- name: Test CER
type: cer
value: 7.775
---
<!-- 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-welsh
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - CY dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2650
- Wer: 0.2702
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3454 | 8.2 | 3000 | 0.4926 | 0.5703 |
| 1.1202 | 16.39 | 6000 | 0.3529 | 0.3944 |
| 1.0058 | 24.59 | 9000 | 0.3143 | 0.3341 |
| 0.9287 | 32.79 | 12000 | 0.2896 | 0.2980 |
| 0.8849 | 40.98 | 15000 | 0.2727 | 0.2798 |
| 0.8665 | 49.18 | 18000 | 0.2662 | 0.2696 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-romanian
|
infinitejoy
| 2022-03-23T18:33:55Z | 471 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"ro",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- ro
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Romanian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ro
metrics:
- name: Test WER
type: wer
value: 14.194
- name: Test CER
type: cer
value: 3.288
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ro
metrics:
- name: Test WER
type: wer
value: 40.869
- name: Test CER
type: cer
value: 12.049
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ro
metrics:
- name: Test WER
type: wer
value: 47.2
---
<!-- 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-romanian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1167
- Wer: 0.1421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1973 | 8.89 | 2000 | 0.4481 | 0.4849 |
| 0.6005 | 17.78 | 4000 | 0.1420 | 0.1777 |
| 0.5248 | 26.67 | 6000 | 0.1303 | 0.1651 |
| 0.4871 | 35.56 | 8000 | 0.1207 | 0.1523 |
| 0.4428 | 44.44 | 10000 | 0.1143 | 0.1425 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-mongolian
|
infinitejoy
| 2022-03-23T18:33:52Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mn",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mn
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Mongolian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: mn
metrics:
- name: Test WER
type: wer
value: 44.709
- name: Test CER
type: cer
value: 13.532
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: mn
metrics:
- name: Test WER
type: wer
value: 76.643
- name: Test CER
type: cer
value: 36.997
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: mn
metrics:
- name: Test WER
type: wer
value: 78.45
---
<!-- 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-mongolian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - MN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6003
- Wer: 0.4473
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3677 | 15.87 | 2000 | 0.6432 | 0.6198 |
| 1.1379 | 31.75 | 4000 | 0.6196 | 0.5592 |
| 1.0093 | 47.62 | 6000 | 0.5828 | 0.5117 |
| 0.8888 | 63.49 | 8000 | 0.5754 | 0.4822 |
| 0.7985 | 79.37 | 10000 | 0.5987 | 0.4690 |
| 0.697 | 95.24 | 12000 | 0.6014 | 0.4471 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-basaa
|
infinitejoy
| 2022-03-23T18:33:50Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"bas",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bas
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Basaa
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: bas
metrics:
- name: Test WER
type: wer
value: 104.08
- name: Test CER
type: cer
value: 228.48
---
<!-- 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-basaa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BAS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5975
- Wer: 0.4981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 200.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.9287 | 15.62 | 500 | 2.8774 | 1.0 |
| 1.1182 | 31.25 | 1000 | 0.6248 | 0.7131 |
| 0.8329 | 46.88 | 1500 | 0.5573 | 0.5792 |
| 0.7109 | 62.5 | 2000 | 0.5420 | 0.5683 |
| 0.6295 | 78.12 | 2500 | 0.5166 | 0.5395 |
| 0.5715 | 93.75 | 3000 | 0.5487 | 0.5629 |
| 0.5016 | 109.38 | 3500 | 0.5370 | 0.5471 |
| 0.4661 | 125.0 | 4000 | 0.5621 | 0.5395 |
| 0.423 | 140.62 | 4500 | 0.5658 | 0.5248 |
| 0.3793 | 156.25 | 5000 | 0.5921 | 0.4981 |
| 0.3651 | 171.88 | 5500 | 0.5987 | 0.4888 |
| 0.3351 | 187.5 | 6000 | 0.6017 | 0.4948 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
LegolasTheElf/Wav2Vec2_xls_r_lm_300m_hi
|
LegolasTheElf
| 2022-03-23T18:33:41Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"Openslr Multilingual",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hi
license: apache-2.0
tags:
- Openslr Multilingual
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: Wav2Vec2_xls_r_300m_hi_final
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 34.21
---
<!-- 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_hi_final
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the ['Openslr Multilingual and code-switching ASR challenge'](http://www.openslr.org/103/) dataset and ['mozilla-foundation/common_voice_7_0'](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3035
- Wer: 0.3137
- Cer: 0.0972
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.9821 | 0.64 | 400 | 0.5059 | 0.4783 | 0.1573 |
| 0.6861 | 1.28 | 800 | 0.4201 | 0.4247 | 0.1356 |
| 0.585 | 1.92 | 1200 | 0.3797 | 0.3811 | 0.1210 |
| 0.5193 | 2.56 | 1600 | 0.3577 | 0.3652 | 0.1152 |
| 0.4583 | 3.21 | 2000 | 0.3422 | 0.3519 | 0.1111 |
| 0.4282 | 3.85 | 2400 | 0.3261 | 0.3450 | 0.1071 |
| 0.3951 | 4.49 | 2800 | 0.3201 | 0.3325 | 0.1048 |
| 0.3619 | 5.13 | 3200 | 0.3167 | 0.3296 | 0.1030 |
| 0.345 | 5.77 | 3600 | 0.3157 | 0.3210 | 0.1013 |
| 0.338 | 6.41 | 4000 | 0.3051 | 0.3143 | 0.0982 |
| 0.3155 | 7.05 | 4400 | 0.3059 | 0.3154 | 0.0986 |
| 0.3057 | 7.69 | 4800 | 0.3035 | 0.3137 | 0.0972 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
AndrewMcDowell/wav2vec2-xls-r-300m-arabic
|
AndrewMcDowell
| 2022-03-23T18:33:36Z | 28 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ar
license: apache-2.0
tags:
- ar
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ar
metrics:
- name: Test WER
type: wer
value: 47.54
- name: Test CER
type: cer
value: 17.64
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 93.72
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ar
metrics:
- name: Test WER
type: wer
value: 92.49
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4502
- Wer: 0.4783
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.7972 | 0.43 | 500 | 5.1401 | 1.0 |
| 3.3241 | 0.86 | 1000 | 3.3220 | 1.0 |
| 3.1432 | 1.29 | 1500 | 3.0806 | 0.9999 |
| 2.9297 | 1.72 | 2000 | 2.5678 | 1.0057 |
| 2.2593 | 2.14 | 2500 | 1.1068 | 0.8218 |
| 2.0504 | 2.57 | 3000 | 0.7878 | 0.7114 |
| 1.937 | 3.0 | 3500 | 0.6955 | 0.6450 |
| 1.8491 | 3.43 | 4000 | 0.6452 | 0.6304 |
| 1.803 | 3.86 | 4500 | 0.5961 | 0.6042 |
| 1.7545 | 4.29 | 5000 | 0.5550 | 0.5748 |
| 1.7045 | 4.72 | 5500 | 0.5374 | 0.5743 |
| 1.6733 | 5.15 | 6000 | 0.5337 | 0.5404 |
| 1.6761 | 5.57 | 6500 | 0.5054 | 0.5266 |
| 1.655 | 6.0 | 7000 | 0.4926 | 0.5243 |
| 1.6252 | 6.43 | 7500 | 0.4946 | 0.5183 |
| 1.6209 | 6.86 | 8000 | 0.4915 | 0.5194 |
| 1.5772 | 7.29 | 8500 | 0.4725 | 0.5104 |
| 1.5602 | 7.72 | 9000 | 0.4726 | 0.5097 |
| 1.5783 | 8.15 | 9500 | 0.4667 | 0.4956 |
| 1.5442 | 8.58 | 10000 | 0.4685 | 0.4937 |
| 1.5597 | 9.01 | 10500 | 0.4708 | 0.4957 |
| 1.5406 | 9.43 | 11000 | 0.4539 | 0.4810 |
| 1.5274 | 9.86 | 11500 | 0.4502 | 0.4783 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
Akashpb13/xlsr_hungarian_new
|
Akashpb13
| 2022-03-23T18:33:33Z | 41 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hu",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hu
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hu
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/xlsr_hungarian_new
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hu
metrics:
- name: Test WER
type: wer
value: 0.2851621517163838
- name: Test CER
type: cer
value: 0.06112982522287432
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hu
metrics:
- name: Test WER
type: wer
value: 0.2851621517163838
- name: Test CER
type: cer
value: 0.06112982522287432
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: hu
metrics:
- name: Test WER
type: wer
value: 47.15
---
# Akashpb13/xlsr_hungarian_new
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets):
- Loss: 0.197464
- Wer: 0.330094
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice hungarian train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000095637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 16
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|------|---------------|-----------------|----------|
| 500 | 4.785300 | 0.952295 | 0.796236 |
| 1000 | 0.535800 | 0.217474 | 0.381613 |
| 1500 | 0.258400 | 0.205524 | 0.345056 |
| 2000 | 0.202800 | 0.198680 | 0.336264 |
| 2500 | 0.182700 | 0.197464 | 0.330094 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Akashpb13/xlsr_hungarian_new --dataset mozilla-foundation/common_voice_8_0 --config hu --split test
```
|
abidlabs/speech-text
|
abidlabs
| 2022-03-23T18:33:30Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-07T19:09:18Z |
---
language: en
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- en
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- robust-speech-event
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 English by Jonatas Grosman
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice en
type: common_voice
args: en
metrics:
- name: Test WER
type: wer
value: 19.06
- name: Test CER
type: cer
value: 7.69
- name: Test WER (+LM)
type: wer
value: 14.81
- name: Test CER (+LM)
type: cer
value: 6.84
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: en
metrics:
- name: Dev WER
type: wer
value: 27.72
- name: Dev CER
type: cer
value: 11.65
- name: Dev WER (+LM)
type: wer
value: 20.85
- name: Dev CER (+LM)
type: cer
value: 11.01
---
# Wav2Vec2-Large-XLSR-53-English
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
## Usage
The model can be used directly (without a language model) as follows...
Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
```python
from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = model.transcribe(audio_paths)
```
Writing your own inference script:
```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english"
SAMPLES = 10
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
```
| Reference | Prediction |
| ------------- | ------------- |
| "SHE'LL BE ALL RIGHT." | SHE'LL BE ALL RIGHT |
| SIX | SIX |
| "ALL'S WELL THAT ENDS WELL." | ALL AS WELL THAT ENDS WELL |
| DO YOU MEAN IT? | DO YOU MEAN IT |
| THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS. | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE BUT STILL CAUSES REGRESSION |
| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOW IS MOSLILLAR GOING TO HANDLE ANDBEWOOTH HIS LIKE Q AND Q |
| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RUSTIAN WASTIN PAN ONTE BATTLY |
| NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING |
| SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUICE IS SAUCE FOR THE GONDER |
| GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD |
## Evaluation
1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test`
```bash
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset mozilla-foundation/common_voice_6_0 --config en --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset speech-recognition-community-v2/dev_data --config en --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{grosman2021wav2vec2-large-xlsr-53-english,
title={XLSR Wav2Vec2 English by Jonatas Grosman},
author={Grosman, Jonatas},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english}},
year={2021}
}
```
|
shivam/wav2vec2-xls-r-hindi
|
shivam
| 2022-03-23T18:33:12Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hi",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hi
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
- cer
model-index:
- name: shivam/wav2vec2-xls-r-hindi
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice Corpus 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 52.3
- name: Test CER
type: cer
value: 26.09
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2282
- Wer: 0.6838
## Evaluation results on Common Voice 7 "test" (Running ./eval.py):
### With LM
- WER: 52.30
- CER: 26.09
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.3155 | 3.4 | 500 | 4.5582 | 1.0 |
| 3.3369 | 6.8 | 1000 | 3.4269 | 1.0 |
| 2.1785 | 10.2 | 1500 | 1.7191 | 0.8831 |
| 1.579 | 13.6 | 2000 | 1.3604 | 0.7647 |
| 1.3773 | 17.01 | 2500 | 1.2737 | 0.7519 |
| 1.3165 | 20.41 | 3000 | 1.2457 | 0.7401 |
| 1.2274 | 23.81 | 3500 | 1.3617 | 0.7301 |
| 1.1787 | 27.21 | 4000 | 1.2068 | 0.7010 |
| 1.1467 | 30.61 | 4500 | 1.2416 | 0.6946 |
| 1.0801 | 34.01 | 5000 | 1.2312 | 0.6990 |
| 1.0709 | 37.41 | 5500 | 1.2984 | 0.7138 |
| 1.0307 | 40.81 | 6000 | 1.2049 | 0.6871 |
| 1.0003 | 44.22 | 6500 | 1.1956 | 0.6841 |
| 1.004 | 47.62 | 7000 | 1.2101 | 0.6793 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
|
sammy786/wav2vec2-xlsr-romansh_vallader
|
sammy786
| 2022-03-23T18:33:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"rm-vallader",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- rm-vallader
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- rm-vallader
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-romansh_vallader
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: rm-vallader
metrics:
- name: Test WER
type: wer
value: 28.54
- name: Test CER
type: cer
value: 6.57
---
# sammy786/wav2vec2-xlsr-romansh_vallader
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - rm-vallader dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 30.31
- Wer: 26.32
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|------|---------------|-----------------|----------|
| 200 | 5.895100 | 3.136624 | 0.999713 |
| 400 | 1.545700 | 0.445069 | 0.471584 |
| 600 | 0.693900 | 0.340700 | 0.363088 |
| 800 | 0.510600 | 0.295432 | 0.289610 |
| 1000 | 0.318800 | 0.286795 | 0.281860 |
| 1200 | 0.194000 | 0.307468 | 0.274110 |
| 1400 | 0.151800 | 0.304849 | 0.264351 |
| 1600 | 0.148300 | 0.303112 | 0.263203 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-romansh_vallader --dataset mozilla-foundation/common_voice_8_0 --config rm-vallader --split test
```
|
samitizerxu/wav2vec2-xls-r-300m-fr
|
samitizerxu
| 2022-03-23T18:33:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"fr",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- fr
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-cls-r-300m-fr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 56.62
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 58.22
---
<!-- 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-cls-r-300m-fr
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 - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6521
- Wer: 0.4330
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6773 | 0.8 | 500 | 1.3907 | 0.9864 |
| 0.9526 | 1.6 | 1000 | 0.7760 | 0.6448 |
| 0.6418 | 2.4 | 1500 | 0.7605 | 0.6194 |
| 0.5028 | 3.2 | 2000 | 0.6516 | 0.5322 |
| 0.4133 | 4.0 | 2500 | 0.6303 | 0.5097 |
| 0.3285 | 4.8 | 3000 | 0.6422 | 0.5062 |
| 0.2764 | 5.6 | 3500 | 0.5936 | 0.4748 |
| 0.2361 | 6.4 | 4000 | 0.6486 | 0.4683 |
| 0.2049 | 7.2 | 4500 | 0.6321 | 0.4532 |
| 0.176 | 8.0 | 5000 | 0.6230 | 0.4482 |
| 0.1393 | 8.8 | 5500 | 0.6595 | 0.4403 |
| 0.1141 | 9.6 | 6000 | 0.6552 | 0.4348 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
huggingtweets/mattiasinspace
|
huggingtweets
| 2022-03-23T18:30:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T18:30:21Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1434246328788398081/M7Httz0A_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mattias in Deep</div>
<div style="text-align: center; font-size: 14px;">@mattiasinspace</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Mattias in Deep.
| Data | Mattias in Deep |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 26 |
| Short tweets | 196 |
| Tweets kept | 3027 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2r9u5eoz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mattiasinspace's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ua0ungm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ua0ungm/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mattiasinspace')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sammy786/wav2vec2-xlsr-mongolian
|
sammy786
| 2022-03-23T18:30:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mn",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mn
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-mongolian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: mn
metrics:
- name: Test WER
type: wer
value: 32.63
- name: Test CER
type: cer
value: 9.26
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: mn
metrics:
- name: Test WER
type: wer
value: 91.26
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: mn
metrics:
- name: Test WER
type: wer
value: 91.37
---
# sammy786/wav2vec2-xlsr-mongolian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - mn dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 31.52
- Wer: 34.1522
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:----:|:-------------:|:---------------:|:--------:|
| 200 | 4.906200 | 3.012986 | 1.000000 |
| 400 | 1.734600 | 0.704821 | 0.750497 |
| 600 | 1.132100 | 0.496223 | 0.531241 |
| 800 | 0.929300 | 0.468937 | 0.469043 |
| 1000 | 0.772300 | 0.425313 | 0.448168 |
| 1200 | 0.623900 | 0.394633 | 0.414229 |
| 1400 | 0.512400 | 0.369225 | 0.397614 |
| 1600 | 0.439900 | 0.346033 | 0.391650 |
| 1800 | 0.391300 | 0.358454 | 0.379296 |
| 2000 | 0.377000 | 0.346822 | 0.359415 |
| 2200 | 0.347500 | 0.325205 | 0.348481 |
| 2400 | 0.343600 | 0.315233 | 0.344078 |
| 2600 | 0.328000 | 0.308826 | 0.341522 |
| 2800 | 0.358200 | 0.331786 | 0.343084 |
| 3000 | 0.417200 | 0.370051 | 0.356433 |
| 3200 | 0.685300 | 0.595438 | 0.407413 |
| 3400 | 0.764100 | 0.643449 | 0.359983 |
| 3600 | 0.717100 | 0.505033 | 0.371911 |
| 3800 | 0.620900 | 0.464138 | 0.369071 |
| 4000 | 0.590700 | 0.445417 | 0.363249 |
| 4200 | 0.561000 | 0.440727 | 0.360267 |
| 4400 | 0.550600 | 0.447122 | 0.360267 |
| 4600 | 0.562100 | 0.457020 | 0.359841 |
| 4800 | 0.578800 | 0.470477 | 0.360551 |
| 5000 | 0.580400 | 0.481413 | 0.362539 |
| 5200 | 0.605500 | 0.485240 | 0.362823 |
| 5400 | 0.582900 | 0.486654 | 0.362965 |
| 5600 | 0.593900 | 0.486715 | 0.363107 |
| 5800 | 0.590900 | 0.486716 | 0.363107 |
| 6000 | 0.587200 | 0.486716 | 0.363107 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-mongolian --dataset mozilla-foundation/common_voice_8_0 --config mn --split test
```
|
infinitejoy/wav2vec2-large-xls-r-300m-urdu
|
infinitejoy
| 2022-03-23T18:30:21Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ur",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
- ur
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Urdu
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ur
metrics:
- name: Test WER
type: wer
value: 105.66
- name: Test CER
type: cer
value: 434.011
---
<!-- 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. -->
infinitejoy/wav2vec2-large-xls-r-300m-urdu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - -UR dataset.
It achieves the following results on the evaluation set:
- Loss: NA
- Wer: NA
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py \
--model_id infinitejoy/wav2vec2-large-xls-r-300m-urdu --dataset speech-recognition-community-v2/dev_data \
--config ur --split validation --chunk_length_s 10 --stride_length_s 1
```
### Inference
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "infinitejoy/wav2vec2-large-xls-r-300m-urdu"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "ur", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
```
### Eval results on Common Voice 7 "test" (WER):
|
vitouphy/wav2vec2-xls-r-300m-japanese
|
vitouphy
| 2022-03-23T18:30:07Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"ja",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"doi:10.57967/hf/0124",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- ja
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Japanese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER
type: wer
value: 54.05
- name: Test CER
type: cer
value: 27.54
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
- name: Validation WER
type: wer
value: 48.77
- name: Validation CER
type: cer
value: 24.87
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ja
metrics:
- name: Test CER
type: cer
value: 27.36
---
#
This model is for transcribing audio into Hiragana, one format of Japanese language.
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `mozilla-foundation/common_voice_8_0 dataset`. Note that the following results are achieved by:
- Modify `eval.py` to suit the use case.
- Since kanji and katakana shares the same sound as hiragana, we convert all texts to hiragana using [pykakasi](https://pykakasi.readthedocs.io) and tokenize them using [fugashi](https://github.com/polm/fugashi).
It achieves the following results on the evaluation set:
- Loss: 0.7751
- Cer: 0.2227
# Evaluation results (Running ./eval.py):
| Model | Metric | Common-Voice-8/test | speech-recognition-community-v2/dev-data |
|:--------:|:------:|:-------------------:|:------------------------------------------:|
| w/o LM | WER | 0.5964 | 0.5532 |
| | CER | 0.2944 | 0.2629 |
| w/ LM | WER | 0.5405 | 0.4877 |
| | CER | **0.2754** | **0.2487** |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.4081 | 1.6 | 500 | 4.0983 | 1.0 |
| 3.303 | 3.19 | 1000 | 3.3563 | 1.0 |
| 3.1538 | 4.79 | 1500 | 3.2066 | 0.9239 |
| 2.1526 | 6.39 | 2000 | 1.1597 | 0.3355 |
| 1.8726 | 7.98 | 2500 | 0.9023 | 0.2505 |
| 1.7817 | 9.58 | 3000 | 0.8219 | 0.2334 |
| 1.7488 | 11.18 | 3500 | 0.7915 | 0.2222 |
| 1.7039 | 12.78 | 4000 | 0.7751 | 0.2227 |
| Stop & Train | | | | |
| 1.6571 | 15.97 | 5000 | 0.6788 | 0.1685 |
| 1.520400 | 19.16 | 6000 | 0.6095 | 0.1409 |
| 1.448200 | 22.35 | 7000 | 0.5843 | 0.1430 |
| 1.385400 | 25.54 | 8000 | 0.5699 | 0.1263 |
| 1.354200 | 28.73 | 9000 | 0.5686 | 0.1219 |
| 1.331500 | 31.92 | 10000 | 0.5502 | 0.1144 |
| 1.290800 | 35.11 | 11000 | 0.5371 | 0.1140 |
| Stop & Train | | | | |
| 1.235200 | 38.30 | 12000 | 0.5394 | 0.1106 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
phantomcoder1996/wav2vec2-large-xls-r-300m-arabic-colab
|
phantomcoder1996
| 2022-03-23T18:30:02Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"ar",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ar
thumbnail: wav2vec2-large-xls-r fine tuned on common voice data for Modern Standard
Arabic
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- WER
model-index:
- name: wav2vec2-large-xls-r-300m-arabic-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: ar
metrics:
- name: Test WER
type: wer
value: 64.38
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 96.15
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ar
metrics:
- name: Test WER
type: wer
value: 94.96
---
|
lgris/wav2vec2-xls-r-1b-cv8
|
lgris
| 2022-03-23T18:29:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-1b-cv8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pt
metrics:
- name: Test WER
type: wer
value: 17.7
- name: Test CER
type: cer
value: 5.21
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 45.68
- name: Test CER
type: cer
value: 18.67
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Test WER
type: wer
value: 45.29
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 48.03
---
<!-- 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-1b-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2007
- Wer: 0.1838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.1172 | 0.32 | 500 | 1.2852 | 0.9783 |
| 1.4152 | 0.64 | 1000 | 0.6434 | 0.6105 |
| 1.4342 | 0.96 | 1500 | 0.4844 | 0.3989 |
| 1.4657 | 1.29 | 2000 | 0.5080 | 0.4490 |
| 1.4961 | 1.61 | 2500 | 0.4764 | 0.4264 |
| 1.4515 | 1.93 | 3000 | 0.4519 | 0.4068 |
| 1.3924 | 2.25 | 3500 | 0.4472 | 0.4132 |
| 1.4524 | 2.57 | 4000 | 0.4455 | 0.3939 |
| 1.4328 | 2.89 | 4500 | 0.4369 | 0.4069 |
| 1.3456 | 3.22 | 5000 | 0.4234 | 0.3774 |
| 1.3725 | 3.54 | 5500 | 0.4387 | 0.3789 |
| 1.3812 | 3.86 | 6000 | 0.4298 | 0.3825 |
| 1.3282 | 4.18 | 6500 | 0.4025 | 0.3703 |
| 1.3326 | 4.5 | 7000 | 0.3917 | 0.3502 |
| 1.3028 | 4.82 | 7500 | 0.3889 | 0.3582 |
| 1.293 | 5.14 | 8000 | 0.3859 | 0.3496 |
| 1.321 | 5.47 | 8500 | 0.3875 | 0.3576 |
| 1.3165 | 5.79 | 9000 | 0.3927 | 0.3589 |
| 1.2701 | 6.11 | 9500 | 0.4058 | 0.3621 |
| 1.2718 | 6.43 | 10000 | 0.4211 | 0.3916 |
| 1.2683 | 6.75 | 10500 | 0.3968 | 0.3620 |
| 1.2643 | 7.07 | 11000 | 0.4128 | 0.3848 |
| 1.2485 | 7.4 | 11500 | 0.3849 | 0.3727 |
| 1.2608 | 7.72 | 12000 | 0.3770 | 0.3474 |
| 1.2388 | 8.04 | 12500 | 0.3774 | 0.3574 |
| 1.2524 | 8.36 | 13000 | 0.3789 | 0.3550 |
| 1.2458 | 8.68 | 13500 | 0.3770 | 0.3410 |
| 1.2505 | 9.0 | 14000 | 0.3638 | 0.3403 |
| 1.2254 | 9.32 | 14500 | 0.3770 | 0.3509 |
| 1.2459 | 9.65 | 15000 | 0.3592 | 0.3349 |
| 1.2049 | 9.97 | 15500 | 0.3600 | 0.3428 |
| 1.2097 | 10.29 | 16000 | 0.3626 | 0.3347 |
| 1.1988 | 10.61 | 16500 | 0.3740 | 0.3269 |
| 1.1671 | 10.93 | 17000 | 0.3548 | 0.3245 |
| 1.1532 | 11.25 | 17500 | 0.3394 | 0.3140 |
| 1.1459 | 11.58 | 18000 | 0.3349 | 0.3156 |
| 1.1511 | 11.9 | 18500 | 0.3272 | 0.3110 |
| 1.1465 | 12.22 | 19000 | 0.3348 | 0.3084 |
| 1.1426 | 12.54 | 19500 | 0.3193 | 0.3027 |
| 1.1278 | 12.86 | 20000 | 0.3318 | 0.3021 |
| 1.149 | 13.18 | 20500 | 0.3169 | 0.2947 |
| 1.114 | 13.5 | 21000 | 0.3224 | 0.2986 |
| 1.1249 | 13.83 | 21500 | 0.3227 | 0.2921 |
| 1.0968 | 14.15 | 22000 | 0.3033 | 0.2878 |
| 1.0851 | 14.47 | 22500 | 0.2996 | 0.2863 |
| 1.0985 | 14.79 | 23000 | 0.3011 | 0.2843 |
| 1.0808 | 15.11 | 23500 | 0.2932 | 0.2759 |
| 1.069 | 15.43 | 24000 | 0.2919 | 0.2750 |
| 1.0602 | 15.76 | 24500 | 0.2959 | 0.2713 |
| 1.0369 | 16.08 | 25000 | 0.2931 | 0.2754 |
| 1.0573 | 16.4 | 25500 | 0.2920 | 0.2722 |
| 1.051 | 16.72 | 26000 | 0.2855 | 0.2632 |
| 1.0279 | 17.04 | 26500 | 0.2850 | 0.2649 |
| 1.0496 | 17.36 | 27000 | 0.2817 | 0.2585 |
| 1.0516 | 17.68 | 27500 | 0.2961 | 0.2635 |
| 1.0244 | 18.01 | 28000 | 0.2781 | 0.2589 |
| 1.0099 | 18.33 | 28500 | 0.2783 | 0.2565 |
| 1.0016 | 18.65 | 29000 | 0.2719 | 0.2537 |
| 1.0157 | 18.97 | 29500 | 0.2621 | 0.2449 |
| 0.9572 | 19.29 | 30000 | 0.2582 | 0.2427 |
| 0.9802 | 19.61 | 30500 | 0.2707 | 0.2468 |
| 0.9577 | 19.94 | 31000 | 0.2563 | 0.2389 |
| 0.9562 | 20.26 | 31500 | 0.2592 | 0.2382 |
| 0.962 | 20.58 | 32000 | 0.2539 | 0.2341 |
| 0.9541 | 20.9 | 32500 | 0.2505 | 0.2288 |
| 0.9587 | 21.22 | 33000 | 0.2486 | 0.2302 |
| 0.9146 | 21.54 | 33500 | 0.2461 | 0.2269 |
| 0.9215 | 21.86 | 34000 | 0.2387 | 0.2228 |
| 0.9105 | 22.19 | 34500 | 0.2405 | 0.2222 |
| 0.8949 | 22.51 | 35000 | 0.2316 | 0.2191 |
| 0.9153 | 22.83 | 35500 | 0.2358 | 0.2180 |
| 0.8907 | 23.15 | 36000 | 0.2369 | 0.2168 |
| 0.8973 | 23.47 | 36500 | 0.2323 | 0.2120 |
| 0.8878 | 23.79 | 37000 | 0.2293 | 0.2104 |
| 0.8818 | 24.12 | 37500 | 0.2302 | 0.2132 |
| 0.8919 | 24.44 | 38000 | 0.2262 | 0.2083 |
| 0.8473 | 24.76 | 38500 | 0.2257 | 0.2040 |
| 0.8516 | 25.08 | 39000 | 0.2246 | 0.2031 |
| 0.8451 | 25.4 | 39500 | 0.2198 | 0.2000 |
| 0.8288 | 25.72 | 40000 | 0.2199 | 0.1990 |
| 0.8465 | 26.05 | 40500 | 0.2165 | 0.1972 |
| 0.8305 | 26.37 | 41000 | 0.2128 | 0.1957 |
| 0.8202 | 26.69 | 41500 | 0.2127 | 0.1937 |
| 0.8223 | 27.01 | 42000 | 0.2100 | 0.1934 |
| 0.8322 | 27.33 | 42500 | 0.2076 | 0.1905 |
| 0.8139 | 27.65 | 43000 | 0.2054 | 0.1880 |
| 0.8299 | 27.97 | 43500 | 0.2026 | 0.1868 |
| 0.7937 | 28.3 | 44000 | 0.2045 | 0.1872 |
| 0.7972 | 28.62 | 44500 | 0.2025 | 0.1861 |
| 0.809 | 28.94 | 45000 | 0.2026 | 0.1858 |
| 0.813 | 29.26 | 45500 | 0.2013 | 0.1838 |
| 0.7718 | 29.58 | 46000 | 0.2010 | 0.1837 |
| 0.7929 | 29.9 | 46500 | 0.2008 | 0.1840 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
|
anuragshas/wav2vec2-xls-r-1b-hi
|
anuragshas
| 2022-03-23T18:29:52Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-1b-hi-cv7
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_7_0
name: Common Voice 7
args: hi
metrics:
- type: wer
value: 18.504
name: Test WER
- name: Test CER
type: cer
value: 6.655
---
<!-- 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-1b-hi-cv7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5878
- Wer: 0.3419
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.9859 | 2.72 | 400 | 1.1663 | 0.7948 |
| 1.2969 | 5.44 | 800 | 0.7725 | 0.6562 |
| 1.1954 | 8.16 | 1200 | 0.5940 | 0.4904 |
| 1.164 | 10.88 | 1600 | 0.5338 | 0.4316 |
| 1.1464 | 13.6 | 2000 | 0.5432 | 0.4226 |
| 1.1553 | 16.33 | 2400 | 0.5471 | 0.4260 |
| 1.0985 | 19.05 | 2800 | 0.5290 | 0.4076 |
| 1.0421 | 21.77 | 3200 | 0.5672 | 0.4181 |
| 0.9831 | 24.49 | 3600 | 0.5741 | 0.4141 |
| 0.9827 | 27.21 | 4000 | 0.5754 | 0.4179 |
| 0.9669 | 29.93 | 4400 | 0.5310 | 0.3889 |
| 0.9496 | 32.65 | 4800 | 0.5649 | 0.4062 |
| 0.9112 | 35.37 | 5200 | 0.5738 | 0.3926 |
| 0.8838 | 38.1 | 5600 | 0.5232 | 0.3768 |
| 0.8666 | 40.81 | 6000 | 0.5510 | 0.3852 |
| 0.8366 | 43.54 | 6400 | 0.5436 | 0.3837 |
| 0.7957 | 46.26 | 6800 | 0.5337 | 0.3775 |
| 0.7834 | 48.98 | 7200 | 0.5611 | 0.3844 |
| 0.7685 | 51.7 | 7600 | 0.5710 | 0.4008 |
| 0.7431 | 54.42 | 8000 | 0.5636 | 0.3726 |
| 0.7353 | 57.14 | 8400 | 0.5937 | 0.3836 |
| 0.7001 | 59.86 | 8800 | 0.5815 | 0.3858 |
| 0.6799 | 62.58 | 9200 | 0.5862 | 0.3696 |
| 0.6459 | 65.31 | 9600 | 0.6181 | 0.3762 |
| 0.6121 | 68.03 | 10000 | 0.5637 | 0.3590 |
| 0.5942 | 70.75 | 10400 | 0.6374 | 0.3882 |
| 0.5769 | 73.47 | 10800 | 0.6015 | 0.3640 |
| 0.5689 | 76.19 | 11200 | 0.5669 | 0.3508 |
| 0.5461 | 78.91 | 11600 | 0.5967 | 0.3621 |
| 0.5286 | 81.63 | 12000 | 0.5840 | 0.3605 |
| 0.5057 | 84.35 | 12400 | 0.5848 | 0.3489 |
| 0.482 | 87.07 | 12800 | 0.5860 | 0.3488 |
| 0.4655 | 89.79 | 13200 | 0.5780 | 0.3453 |
| 0.4523 | 92.52 | 13600 | 0.6150 | 0.3532 |
| 0.4422 | 95.24 | 14000 | 0.5930 | 0.3452 |
| 0.4436 | 97.96 | 14400 | 0.5867 | 0.3428 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-1b-hi --dataset mozilla-foundation/common_voice_7_0 --config hi --split test
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-xls-r-1b-hi"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "hi", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "तुम्हारे पास तीन महीने बचे हैं"
```
### Eval results on Common Voice 7 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 28.942 | 18.504 |
|
RuudVelo/wav2vec2-large-xls-r-300m-nl
|
RuudVelo
| 2022-03-23T18:29:49Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"nl",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- nl
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-nl
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
args: nl
metrics:
- name: Test WER
type: wer
value: 17.17
- name: Test CER
type: cer
value: 5.13
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: nl
metrics:
- name: Test WER
type: wer
value: 35.76
- name: Test CER
type: cer
value: 13.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: nl
metrics:
- name: Test WER
type: wer
value: 37.19
---
<!-- 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-nl
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 test set:
- Loss: 0.3923
- Wer: 0.1748
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.5787 | 0.89 | 400 | 0.6354 | 0.5643 |
| 0.3036 | 1.78 | 800 | 0.3690 | 0.3552 |
| 0.188 | 2.67 | 1200 | 0.3239 | 0.2958 |
| 0.1434 | 3.56 | 1600 | 0.3093 | 0.2515 |
| 0.1245 | 4.44 | 2000 | 0.3024 | 0.2433 |
| 0.1095 | 5.33 | 2400 | 0.3249 | 0.2643 |
| 0.0979 | 6.22 | 2800 | 0.3191 | 0.2281 |
| 0.0915 | 7.11 | 3200 | 0.3152 | 0.2216 |
| 0.0829 | 8.0 | 3600 | 0.3419 | 0.2218 |
| 0.0777 | 8.89 | 4000 | 0.3432 | 0.2132 |
| 0.073 | 9.78 | 4400 | 0.3223 | 0.2131 |
| 0.0688 | 10.67 | 4800 | 0.3094 | 0.2152 |
| 0.0647 | 11.56 | 5200 | 0.3411 | 0.2152 |
| 0.0639 | 12.44 | 5600 | 0.3762 | 0.2135 |
| 0.0599 | 13.33 | 6000 | 0.3790 | 0.2137 |
| 0.0572 | 14.22 | 6400 | 0.3693 | 0.2118 |
| 0.0563 | 15.11 | 6800 | 0.3495 | 0.2139 |
| 0.0521 | 16.0 | 7200 | 0.3800 | 0.2023 |
| 0.0508 | 16.89 | 7600 | 0.3678 | 0.2033 |
| 0.0513 | 17.78 | 8000 | 0.3845 | 0.1987 |
| 0.0476 | 18.67 | 8400 | 0.3511 | 0.2037 |
| 0.045 | 19.56 | 8800 | 0.3794 | 0.1994 |
| 0.044 | 20.44 | 9200 | 0.3525 | 0.2050 |
| 0.043 | 21.33 | 9600 | 0.4082 | 0.2007 |
| 0.0409 | 22.22 | 10000 | 0.3866 | 0.2004 |
| 0.0393 | 23.11 | 10400 | 0.3899 | 0.2008 |
| 0.0382 | 24.0 | 10800 | 0.3626 | 0.1951 |
| 0.039 | 24.89 | 11200 | 0.3936 | 0.1953 |
| 0.0361 | 25.78 | 11600 | 0.4262 | 0.1928 |
| 0.0362 | 26.67 | 12000 | 0.3796 | 0.1934 |
| 0.033 | 27.56 | 12400 | 0.3616 | 0.1934 |
| 0.0321 | 28.44 | 12800 | 0.3742 | 0.1933 |
| 0.0325 | 29.33 | 13200 | 0.3582 | 0.1869 |
| 0.0309 | 30.22 | 13600 | 0.3717 | 0.1874 |
| 0.029 | 31.11 | 14000 | 0.3814 | 0.1894 |
| 0.0296 | 32.0 | 14400 | 0.3698 | 0.1877 |
| 0.0281 | 32.89 | 14800 | 0.3976 | 0.1899 |
| 0.0275 | 33.78 | 15200 | 0.3854 | 0.1858 |
| 0.0264 | 34.67 | 15600 | 0.4021 | 0.1889 |
| 0.0261 | 35.56 | 16000 | 0.3850 | 0.1830 |
| 0.0242 | 36.44 | 16400 | 0.4091 | 0.1878 |
| 0.0245 | 37.33 | 16800 | 0.4012 | 0.1846 |
| 0.0243 | 38.22 | 17200 | 0.3996 | 0.1833 |
| 0.0223 | 39.11 | 17600 | 0.3962 | 0.1815 |
| 0.0223 | 40.0 | 18000 | 0.3898 | 0.1832 |
| 0.0219 | 40.89 | 18400 | 0.4019 | 0.1822 |
| 0.0211 | 41.78 | 18800 | 0.4035 | 0.1809 |
| 0.021 | 42.67 | 19200 | 0.3915 | 0.1826 |
| 0.0208 | 43.56 | 19600 | 0.3934 | 0.1784 |
| 0.0188 | 44.44 | 20000 | 0.3912 | 0.1787 |
| 0.0195 | 45.33 | 20400 | 0.3989 | 0.1766 |
| 0.0186 | 46.22 | 20800 | 0.3887 | 0.1773 |
| 0.0188 | 47.11 | 21200 | 0.3982 | 0.1758 |
| 0.0175 | 48.0 | 21600 | 0.3933 | 0.1755 |
| 0.0172 | 48.89 | 22000 | 0.3921 | 0.1749 |
| 0.0187 | 49.78 | 22400 | 0.3923 | 0.1748 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
ubamba98/wav2vec2-xls-r-300m-CV8-ro
|
ubamba98
| 2022-03-23T18:29:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"ro",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ro
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
model-index:
- name: wav2vec2-xls-r-300m-CV8-ro
results: []
---
<!-- 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-CV8-ro
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1578
- Wer: 0.6040
- Cer: 0.0475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 2.9736 | 3.62 | 500 | 2.9508 | 1.0 | 1.0 |
| 1.3293 | 7.25 | 1000 | 0.3330 | 0.8407 | 0.0862 |
| 0.956 | 10.87 | 1500 | 0.2042 | 0.6872 | 0.0602 |
| 0.9509 | 14.49 | 2000 | 0.2184 | 0.7088 | 0.0652 |
| 0.9272 | 18.12 | 2500 | 0.2312 | 0.7211 | 0.0703 |
| 0.8561 | 21.74 | 3000 | 0.2158 | 0.6838 | 0.0631 |
| 0.8258 | 25.36 | 3500 | 0.1970 | 0.6844 | 0.0601 |
| 0.7993 | 28.98 | 4000 | 0.1895 | 0.6698 | 0.0577 |
| 0.7525 | 32.61 | 4500 | 0.1845 | 0.6453 | 0.0550 |
| 0.7211 | 36.23 | 5000 | 0.1781 | 0.6274 | 0.0531 |
| 0.677 | 39.85 | 5500 | 0.1732 | 0.6188 | 0.0514 |
| 0.6517 | 43.48 | 6000 | 0.1691 | 0.6177 | 0.0503 |
| 0.6326 | 47.1 | 6500 | 0.1619 | 0.6045 | 0.0479 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
ubamba98/wav2vec2-xls-r-1b-ro
|
ubamba98
| 2022-03-23T18:29:42Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ro",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-xls-r-1b-ro
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: ro
metrics:
- name: Test WER
type: wer
value: 99.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ro
metrics:
- name: Test WER
type: wer
value: 99.98
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ro
metrics:
- name: Test WER
type: wer
value: 99.99
---
<!-- 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-1b-ro
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1113
- Wer: 0.4770
- Cer: 0.0306
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.7844 | 1.67 | 1500 | 0.3412 | 0.8600 | 0.0940 |
| 0.7272 | 3.34 | 3000 | 0.1926 | 0.6409 | 0.0527 |
| 0.6924 | 5.02 | 4500 | 0.1413 | 0.5722 | 0.0401 |
| 0.6327 | 6.69 | 6000 | 0.1252 | 0.5366 | 0.0371 |
| 0.6363 | 8.36 | 7500 | 0.1235 | 0.5741 | 0.0389 |
| 0.6238 | 10.03 | 9000 | 0.1180 | 0.5542 | 0.0362 |
| 0.6018 | 11.71 | 10500 | 0.1192 | 0.5694 | 0.0369 |
| 0.583 | 13.38 | 12000 | 0.1216 | 0.5772 | 0.0385 |
| 0.5643 | 15.05 | 13500 | 0.1195 | 0.5419 | 0.0371 |
| 0.5399 | 16.72 | 15000 | 0.1240 | 0.5224 | 0.0370 |
| 0.5529 | 18.39 | 16500 | 0.1174 | 0.5555 | 0.0367 |
| 0.5246 | 20.07 | 18000 | 0.1097 | 0.5047 | 0.0339 |
| 0.4936 | 21.74 | 19500 | 0.1225 | 0.5189 | 0.0382 |
| 0.4629 | 23.41 | 21000 | 0.1142 | 0.5047 | 0.0344 |
| 0.4463 | 25.08 | 22500 | 0.1168 | 0.4887 | 0.0339 |
| 0.4671 | 26.76 | 24000 | 0.1119 | 0.5073 | 0.0338 |
| 0.4359 | 28.43 | 25500 | 0.1206 | 0.5479 | 0.0363 |
| 0.4225 | 30.1 | 27000 | 0.1122 | 0.5170 | 0.0345 |
| 0.4038 | 31.77 | 28500 | 0.1159 | 0.5032 | 0.0343 |
| 0.4271 | 33.44 | 30000 | 0.1116 | 0.5126 | 0.0339 |
| 0.3867 | 35.12 | 31500 | 0.1101 | 0.4937 | 0.0327 |
| 0.3674 | 36.79 | 33000 | 0.1142 | 0.4940 | 0.0330 |
| 0.3607 | 38.46 | 34500 | 0.1106 | 0.5145 | 0.0327 |
| 0.3651 | 40.13 | 36000 | 0.1172 | 0.4921 | 0.0317 |
| 0.3268 | 41.81 | 37500 | 0.1093 | 0.4830 | 0.0310 |
| 0.3345 | 43.48 | 39000 | 0.1131 | 0.4760 | 0.0314 |
| 0.3236 | 45.15 | 40500 | 0.1132 | 0.4864 | 0.0317 |
| 0.312 | 46.82 | 42000 | 0.1124 | 0.4861 | 0.0315 |
| 0.3106 | 48.49 | 43500 | 0.1116 | 0.4745 | 0.0306 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
cahya/xls-r-ab-test
|
cahya
| 2022-03-23T18:29:37Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"ab",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ab
tags:
- ab
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 135.4675
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.10.3
|
shivam/xls-r-300m-marathi
|
shivam
| 2022-03-23T18:29:32Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"mr",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mr
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- mr
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: ''
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice Corpus 8.0
type: mozilla-foundation/common_voice_8_0
args: mr
metrics:
- name: Test WER
type: wer
value: 38.27
- name: Test CER
type: cer
value: 8.91
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset.
It achieves the following results on the mozilla-foundation/common_voice_8_0 mr test set:
- Without LM
+ WER: 48.53
+ CER: 10.63
- With LM
+ WER: 38.27
+ CER: 8.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 400.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.2706 | 22.73 | 500 | 4.0174 | 1.0 |
| 3.2492 | 45.45 | 1000 | 3.2309 | 0.9908 |
| 1.9709 | 68.18 | 1500 | 1.0651 | 0.8440 |
| 1.4088 | 90.91 | 2000 | 0.5765 | 0.6550 |
| 1.1326 | 113.64 | 2500 | 0.4842 | 0.5760 |
| 0.9709 | 136.36 | 3000 | 0.4785 | 0.6013 |
| 0.8433 | 159.09 | 3500 | 0.5048 | 0.5419 |
| 0.7404 | 181.82 | 4000 | 0.5052 | 0.5339 |
| 0.6589 | 204.55 | 4500 | 0.5237 | 0.5897 |
| 0.5831 | 227.27 | 5000 | 0.5166 | 0.5447 |
| 0.5375 | 250.0 | 5500 | 0.5292 | 0.5487 |
| 0.4784 | 272.73 | 6000 | 0.5480 | 0.5596 |
| 0.4421 | 295.45 | 6500 | 0.5682 | 0.5467 |
| 0.4047 | 318.18 | 7000 | 0.5681 | 0.5447 |
| 0.3779 | 340.91 | 7500 | 0.5783 | 0.5347 |
| 0.3525 | 363.64 | 8000 | 0.5856 | 0.5367 |
| 0.3393 | 386.36 | 8500 | 0.5960 | 0.5359 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
|
reach-vb/wav2vec2-large-xls-r-1B-common_voice-sl-ft
|
reach-vb
| 2022-03-23T18:29:30Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"sl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- sl
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-1B-common_voice-sl-ft
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: lv
metrics:
- name: Test WER
type: wer
value: 23.26
- name: Test CER
type: cer
value: 7.95
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: sl
metrics:
- name: Test WER
type: wer
value: 13.59
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Test WER
type: wer
value: 62.71
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 62.34
---
<!-- 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-1B-common_voice-sl-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2112
- Wer: 0.1404
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.8291 | 12.2 | 500 | 0.5674 | 0.7611 |
| 0.0416 | 24.39 | 1000 | 0.3093 | 0.2964 |
| 0.0256 | 36.59 | 1500 | 0.2224 | 0.2072 |
| 0.0179 | 48.78 | 2000 | 0.2274 | 0.1960 |
| 0.0113 | 60.98 | 2500 | 0.2078 | 0.1582 |
| 0.0086 | 73.17 | 3000 | 0.1898 | 0.1552 |
| 0.0059 | 85.37 | 3500 | 0.2054 | 0.1446 |
| 0.0044 | 97.56 | 4000 | 0.2112 | 0.1404 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
|
anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm
|
anuragshas
| 2022-03-23T18:29:27Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sl
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
model-index:
- name: XLS-R-300M - Slovenian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sl
metrics:
- name: Test WER
type: wer
value: 12.736
- name: Test CER
type: cer
value: 3.605
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Test WER
type: wer
value: 45.587
- name: Test CER
type: cer
value: 20.886
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 45.42
---
<!-- 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. -->
# XLS-R-300M - Slovenian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2578
- Wer: 0.2273
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1829 | 4.88 | 400 | 3.1228 | 1.0 |
| 2.8675 | 9.76 | 800 | 2.8616 | 0.9993 |
| 1.583 | 14.63 | 1200 | 0.6392 | 0.6239 |
| 1.1959 | 19.51 | 1600 | 0.3602 | 0.3651 |
| 1.0276 | 24.39 | 2000 | 0.3021 | 0.2981 |
| 0.9671 | 29.27 | 2400 | 0.2872 | 0.2739 |
| 0.873 | 34.15 | 2800 | 0.2593 | 0.2459 |
| 0.8513 | 39.02 | 3200 | 0.2617 | 0.2473 |
| 0.8132 | 43.9 | 3600 | 0.2548 | 0.2426 |
| 0.7935 | 48.78 | 4000 | 0.2637 | 0.2353 |
| 0.7565 | 53.66 | 4400 | 0.2629 | 0.2322 |
| 0.7359 | 58.54 | 4800 | 0.2579 | 0.2253 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config sl --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sl", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "zmago je divje od letel s helikopterjem visoko vzrak"
```
### Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 19.938 | 12.736 |
|
anantoj/wav2vec2-xls-r-1b-korean
|
anantoj
| 2022-03-23T18:29:13Z | 37 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ko",
"dataset:kresnik/zeroth_korean",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: ko
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- kresnik/zeroth_korean
model-index:
- name: Wav2Vec2 XLS-R 1B Korean
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ko
metrics:
- name: Test WER
type: wer
value: 82.07
- name: Test CER
type: cer
value: 42.12
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ko
metrics:
- name: Test WER
type: wer
value: 82.09
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0639
- Wer: 0.0449
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.603 | 0.72 | 500 | 4.6572 | 0.9985 |
| 2.6314 | 1.44 | 1000 | 2.0424 | 0.9256 |
| 2.2708 | 2.16 | 1500 | 0.9889 | 0.6989 |
| 2.1769 | 2.88 | 2000 | 0.8366 | 0.6312 |
| 2.1142 | 3.6 | 2500 | 0.7555 | 0.5998 |
| 2.0084 | 4.32 | 3000 | 0.7144 | 0.6003 |
| 1.9272 | 5.04 | 3500 | 0.6311 | 0.5461 |
| 1.8687 | 5.75 | 4000 | 0.6252 | 0.5430 |
| 1.8186 | 6.47 | 4500 | 0.5491 | 0.4988 |
| 1.7364 | 7.19 | 5000 | 0.5463 | 0.4959 |
| 1.6809 | 7.91 | 5500 | 0.4724 | 0.4484 |
| 1.641 | 8.63 | 6000 | 0.4679 | 0.4461 |
| 1.572 | 9.35 | 6500 | 0.4387 | 0.4236 |
| 1.5256 | 10.07 | 7000 | 0.3970 | 0.4003 |
| 1.5044 | 10.79 | 7500 | 0.3690 | 0.3893 |
| 1.4563 | 11.51 | 8000 | 0.3752 | 0.3875 |
| 1.394 | 12.23 | 8500 | 0.3386 | 0.3567 |
| 1.3641 | 12.95 | 9000 | 0.3290 | 0.3467 |
| 1.2878 | 13.67 | 9500 | 0.2893 | 0.3135 |
| 1.2602 | 14.39 | 10000 | 0.2723 | 0.3029 |
| 1.2302 | 15.11 | 10500 | 0.2603 | 0.2989 |
| 1.1865 | 15.83 | 11000 | 0.2440 | 0.2794 |
| 1.1491 | 16.55 | 11500 | 0.2500 | 0.2788 |
| 1.093 | 17.27 | 12000 | 0.2279 | 0.2629 |
| 1.0367 | 17.98 | 12500 | 0.2076 | 0.2443 |
| 0.9954 | 18.7 | 13000 | 0.1844 | 0.2259 |
| 0.99 | 19.42 | 13500 | 0.1794 | 0.2179 |
| 0.9385 | 20.14 | 14000 | 0.1765 | 0.2122 |
| 0.8952 | 20.86 | 14500 | 0.1706 | 0.1974 |
| 0.8841 | 21.58 | 15000 | 0.1791 | 0.1969 |
| 0.847 | 22.3 | 15500 | 0.1780 | 0.2060 |
| 0.8669 | 23.02 | 16000 | 0.1608 | 0.1862 |
| 0.8066 | 23.74 | 16500 | 0.1447 | 0.1626 |
| 0.7908 | 24.46 | 17000 | 0.1457 | 0.1655 |
| 0.7459 | 25.18 | 17500 | 0.1350 | 0.1445 |
| 0.7218 | 25.9 | 18000 | 0.1276 | 0.1421 |
| 0.703 | 26.62 | 18500 | 0.1177 | 0.1302 |
| 0.685 | 27.34 | 19000 | 0.1147 | 0.1305 |
| 0.6811 | 28.06 | 19500 | 0.1128 | 0.1244 |
| 0.6444 | 28.78 | 20000 | 0.1120 | 0.1213 |
| 0.6323 | 29.5 | 20500 | 0.1137 | 0.1166 |
| 0.5998 | 30.22 | 21000 | 0.1051 | 0.1107 |
| 0.5706 | 30.93 | 21500 | 0.1035 | 0.1037 |
| 0.5555 | 31.65 | 22000 | 0.1031 | 0.0927 |
| 0.5389 | 32.37 | 22500 | 0.0997 | 0.0900 |
| 0.5201 | 33.09 | 23000 | 0.0920 | 0.0912 |
| 0.5146 | 33.81 | 23500 | 0.0929 | 0.0947 |
| 0.515 | 34.53 | 24000 | 0.1000 | 0.0953 |
| 0.4743 | 35.25 | 24500 | 0.0922 | 0.0892 |
| 0.4707 | 35.97 | 25000 | 0.0852 | 0.0808 |
| 0.4456 | 36.69 | 25500 | 0.0855 | 0.0779 |
| 0.443 | 37.41 | 26000 | 0.0843 | 0.0738 |
| 0.4388 | 38.13 | 26500 | 0.0816 | 0.0699 |
| 0.4162 | 38.85 | 27000 | 0.0752 | 0.0645 |
| 0.3979 | 39.57 | 27500 | 0.0761 | 0.0621 |
| 0.3889 | 40.29 | 28000 | 0.0771 | 0.0625 |
| 0.3923 | 41.01 | 28500 | 0.0755 | 0.0598 |
| 0.3693 | 41.73 | 29000 | 0.0730 | 0.0578 |
| 0.3642 | 42.45 | 29500 | 0.0739 | 0.0598 |
| 0.3532 | 43.17 | 30000 | 0.0712 | 0.0553 |
| 0.3513 | 43.88 | 30500 | 0.0762 | 0.0516 |
| 0.3349 | 44.6 | 31000 | 0.0731 | 0.0504 |
| 0.3305 | 45.32 | 31500 | 0.0725 | 0.0507 |
| 0.3285 | 46.04 | 32000 | 0.0709 | 0.0489 |
| 0.3179 | 46.76 | 32500 | 0.0667 | 0.0467 |
| 0.3158 | 47.48 | 33000 | 0.0653 | 0.0494 |
| 0.3033 | 48.2 | 33500 | 0.0638 | 0.0456 |
| 0.3023 | 48.92 | 34000 | 0.0644 | 0.0464 |
| 0.2975 | 49.64 | 34500 | 0.0643 | 0.0455 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
|
samitizerxu/wav2vec2-xls-r-300m-eo
|
samitizerxu
| 2022-03-23T18:29:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"eo",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- eo
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- eo
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-eo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: eo
metrics:
- name: Test WER
type: wer
value: 34.72
- name: Test CER
type: cer
value: 7.54
---
<!-- 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-eo
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 - EO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2584
- Wer: 0.3114
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.1701 | 0.8 | 500 | 2.8105 | 1.0 |
| 1.9143 | 1.6 | 1000 | 0.5977 | 0.7002 |
| 1.1259 | 2.4 | 1500 | 0.5063 | 0.6157 |
| 0.9732 | 3.2 | 2000 | 0.4264 | 0.5673 |
| 0.8983 | 4.0 | 2500 | 0.4249 | 0.4902 |
| 0.8507 | 4.8 | 3000 | 0.3811 | 0.4536 |
| 0.8064 | 5.6 | 3500 | 0.3643 | 0.4467 |
| 0.7866 | 6.4 | 4000 | 0.3600 | 0.4453 |
| 0.7773 | 7.2 | 4500 | 0.3724 | 0.4470 |
| 0.747 | 8.0 | 5000 | 0.3501 | 0.4189 |
| 0.7279 | 8.8 | 5500 | 0.3500 | 0.4261 |
| 0.7153 | 9.6 | 6000 | 0.3328 | 0.3966 |
| 0.7 | 10.4 | 6500 | 0.3314 | 0.3869 |
| 0.6784 | 11.2 | 7000 | 0.3396 | 0.4051 |
| 0.6582 | 12.0 | 7500 | 0.3236 | 0.3899 |
| 0.6478 | 12.8 | 8000 | 0.3263 | 0.3832 |
| 0.6277 | 13.6 | 8500 | 0.3139 | 0.3769 |
| 0.6053 | 14.4 | 9000 | 0.2955 | 0.3536 |
| 0.5777 | 15.2 | 9500 | 0.2793 | 0.3413 |
| 0.5631 | 16.0 | 10000 | 0.2789 | 0.3353 |
| 0.5446 | 16.8 | 10500 | 0.2709 | 0.3264 |
| 0.528 | 17.6 | 11000 | 0.2693 | 0.3234 |
| 0.5169 | 18.4 | 11500 | 0.2656 | 0.3193 |
| 0.5041 | 19.2 | 12000 | 0.2575 | 0.3102 |
| 0.4971 | 20.0 | 12500 | 0.2584 | 0.3114 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id samitizerxu/wav2vec2-xls-r-300m-eo --dataset mozilla-foundation/common_voice_7_0 --config eo --split test
```
|
reichenbach/wav2vec2-large-xls-r-300m-pa-in
|
reichenbach
| 2022-03-23T18:28:40Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- pa
- pa-IN
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-pa-in
results: []
---
<!-- 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-pa-in
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: 1.9680
- Wer: 0.7283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 180
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 8.2615 | 24.97 | 400 | 3.4784 | 1.0 |
| 3.366 | 49.97 | 800 | 2.3662 | 0.9917 |
| 1.1678 | 74.97 | 1200 | 1.4806 | 0.7709 |
| 0.5496 | 99.97 | 1600 | 1.7166 | 0.7476 |
| 0.4101 | 124.97 | 2000 | 1.8473 | 0.7510 |
| 0.3317 | 149.97 | 2400 | 1.9177 | 0.7322 |
| 0.2956 | 174.97 | 2800 | 1.9680 | 0.7283 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
### Evaluations Result
- WER: 0.7539
- CER: 0.2928
|
manifoldix/xlsr-fa-lm
|
manifoldix
| 2022-03-23T18:28:30Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"fa",
"dataset:common_voice",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: fa
datasets:
- common_voice
tags:
- hf-asr-leaderboard
- robust-speech-event
widget:
- example_title: Common Voice sample 2978
src: https://huggingface.co/manifoldix/xlsr-fa-lm/resolve/main/sample2978.flac
- example_title: Common Voice sample 5168
src: https://huggingface.co/manifoldix/xlsr-fa-lm/resolve/main/sample5168.flac
model-index:
- name: XLS-R-300m Wav2Vec2 Persian
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice fa
type: common_voice
args: fa
metrics:
- name: Test WER without LM
type: wer
value: 26%
- name: Test WER with LM
type: wer
value: 23%
---
## XLSR-300m Persian
Fine-tuned on commom voice FA
|
infinitejoy/wav2vec2-large-xls-r-300m-arabic
|
infinitejoy
| 2022-03-23T18:28:27Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ar
license: apache-2.0
tags:
- ar
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ar
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- 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. -->
# XLS-R-300m-SV
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AR dataset.
It achieves the following results on the evaluation set:
- Loss: NA
- Wer: NA
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py \
--model_id infinitejoy/wav2vec2-large-xls-r-300m-arabic \
--dataset mozilla-foundation/common_voice_7_0 --config ar --split test --log_outputs
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py \
--model_id infinitejoy/wav2vec2-large-xls-r-300m-arabic --dataset speech-recognition-community-v2/dev_data \
--config ar --split validation --chunk_length_s 10 --stride_length_s 1
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "infinitejoy/wav2vec2-large-xls-r-300m-arabic"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "ar", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
```
### Eval results on Common Voice 7 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| NA | NA |
|
emre/wav2vec2-xls-r-300m-Russian-small
|
emre
| 2022-03-23T18:28:22Z | 19 | 2 |
transformers
|
[
"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"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- ru
tags:
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-Russian-small
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ru
type: common_voice
args: ru
metrics:
- name: Test WER
type: wer
value: 48.38
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ru
metrics:
- name: Test WER
type: wer
value: 58.25
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ru
metrics:
- name: Test WER
type: wer
value: 56.83
---
<!-- 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/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
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.512 | 1.32 | 400 | 3.2207 | 1.0 |
| 3.1562 | 2.65 | 800 | 3.0166 | 1.0 |
| 1.5211 | 3.97 | 1200 | 0.7134 | 0.8275 |
| 0.6724 | 5.3 | 1600 | 0.4713 | 0.6402 |
| 0.4693 | 6.62 | 2000 | 0.3904 | 0.5668 |
| 0.3693 | 7.95 | 2400 | 0.3609 | 0.5121 |
| 0.3004 | 9.27 | 2800 | 0.3514 | 0.4838 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
edugp/wav2vec2-xls-r-300m-36-tokens-with-lm-es
|
edugp
| 2022-03-23T18:28:19Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- es
tags:
- es
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-36-tokens-with-lm-es
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice es
type: common_voice
args: es
metrics:
- name: Test WER
type: wer
value: 0.08677014042867702
- name: Test CER
type: cer
value: 0.02810974186831335
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: es
metrics:
- name: Test WER
type: wer
value: 31.68
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: es
metrics:
- name: Test WER
type: wer
value: 34.45
---
# Wav2Vec2-xls-r-300m-36-tokens-with-lm-es
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
This model 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:
- Wer: 0.0868
- Cer: 0.0281
This model consists of a Wav2Vec2 model with an additional KenLM 5-gram language model for CTC decoding.
The model is trained removing all characters that are not lower-case unaccented letters between `a-z` or the Spanish accented vowels `á`, `é`, `í`, `ó`, `ú` or the dieresis on the vowel `u` - `ü`.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 3.6512 | 0.07 | 400 | 0.5734 | 0.4325 |
| 0.4404 | 0.14 | 800 | 0.3329 | 0.3021 |
| 0.3465 | 0.22 | 1200 | 0.3067 | 0.2871 |
| 0.3214 | 0.29 | 1600 | 0.2808 | 0.2694 |
| 0.319 | 0.36 | 2000 | 0.2755 | 0.2677 |
| 0.3015 | 0.43 | 2400 | 0.2667 | 0.2437 |
| 0.3102 | 0.51 | 2800 | 0.2679 | 0.2475 |
| 0.2955 | 0.58 | 3200 | 0.2591 | 0.2421 |
| 0.292 | 0.65 | 3600 | 0.2547 | 0.2404 |
| 0.2961 | 0.72 | 4000 | 0.2824 | 0.2716 |
| 0.2906 | 0.8 | 4400 | 0.2531 | 0.2321 |
| 0.2886 | 0.87 | 4800 | 0.2668 | 0.2573 |
| 0.2934 | 0.94 | 5200 | 0.2608 | 0.2454 |
| 0.2844 | 1.01 | 5600 | 0.2414 | 0.2233 |
| 0.2649 | 1.09 | 6000 | 0.2412 | 0.2198 |
| 0.2587 | 1.16 | 6400 | 0.2432 | 0.2211 |
| 0.2631 | 1.23 | 6800 | 0.2414 | 0.2225 |
| 0.2584 | 1.3 | 7200 | 0.2489 | 0.2290 |
| 0.2588 | 1.37 | 7600 | 0.2341 | 0.2156 |
| 0.2581 | 1.45 | 8000 | 0.2323 | 0.2155 |
| 0.2603 | 1.52 | 8400 | 0.2423 | 0.2231 |
| 0.2527 | 1.59 | 8800 | 0.2381 | 0.2192 |
| 0.2588 | 1.66 | 9200 | 0.2323 | 0.2176 |
| 0.2543 | 1.74 | 9600 | 0.2391 | 0.2151 |
| 0.2528 | 1.81 | 10000 | 0.2295 | 0.2091 |
| 0.2535 | 1.88 | 10400 | 0.2317 | 0.2099 |
| 0.2501 | 1.95 | 10800 | 0.2225 | 0.2105 |
| 0.2441 | 2.03 | 11200 | 0.2356 | 0.2180 |
| 0.2275 | 2.1 | 11600 | 0.2341 | 0.2115 |
| 0.2281 | 2.17 | 12000 | 0.2269 | 0.2117 |
| 0.227 | 2.24 | 12400 | 0.2367 | 0.2125 |
| 0.2471 | 2.32 | 12800 | 0.2307 | 0.2090 |
| 0.229 | 2.39 | 13200 | 0.2231 | 0.2005 |
| 0.2325 | 2.46 | 13600 | 0.2243 | 0.2100 |
| 0.2314 | 2.53 | 14000 | 0.2252 | 0.2098 |
| 0.2309 | 2.6 | 14400 | 0.2269 | 0.2089 |
| 0.2267 | 2.68 | 14800 | 0.2155 | 0.1976 |
| 0.225 | 2.75 | 15200 | 0.2263 | 0.2067 |
| 0.2309 | 2.82 | 15600 | 0.2196 | 0.2041 |
| 0.225 | 2.89 | 16000 | 0.2212 | 0.2052 |
| 0.228 | 2.97 | 16400 | 0.2192 | 0.2028 |
| 0.2136 | 3.04 | 16800 | 0.2169 | 0.2042 |
| 0.2038 | 3.11 | 17200 | 0.2173 | 0.1998 |
| 0.2035 | 3.18 | 17600 | 0.2185 | 0.2002 |
| 0.207 | 3.26 | 18000 | 0.2358 | 0.2120 |
| 0.2102 | 3.33 | 18400 | 0.2213 | 0.2019 |
| 0.211 | 3.4 | 18800 | 0.2176 | 0.1980 |
| 0.2099 | 3.47 | 19200 | 0.2186 | 0.1960 |
| 0.2093 | 3.55 | 19600 | 0.2208 | 0.2016 |
| 0.2046 | 3.62 | 20000 | 0.2138 | 0.1960 |
| 0.2095 | 3.69 | 20400 | 0.2222 | 0.2023 |
| 0.2106 | 3.76 | 20800 | 0.2159 | 0.1964 |
| 0.2066 | 3.83 | 21200 | 0.2083 | 0.1931 |
| 0.2119 | 3.91 | 21600 | 0.2130 | 0.1957 |
| 0.2167 | 3.98 | 22000 | 0.2210 | 0.1987 |
| 0.1973 | 4.05 | 22400 | 0.2112 | 0.1930 |
| 0.1917 | 4.12 | 22800 | 0.2107 | 0.1891 |
| 0.1903 | 4.2 | 23200 | 0.2132 | 0.1911 |
| 0.1903 | 4.27 | 23600 | 0.2077 | 0.1883 |
| 0.1914 | 4.34 | 24000 | 0.2054 | 0.1901 |
| 0.1943 | 4.41 | 24400 | 0.2059 | 0.1885 |
| 0.1943 | 4.49 | 24800 | 0.2095 | 0.1899 |
| 0.1936 | 4.56 | 25200 | 0.2078 | 0.1879 |
| 0.1963 | 4.63 | 25600 | 0.2018 | 0.1884 |
| 0.1934 | 4.7 | 26000 | 0.2034 | 0.1872 |
| 0.2011 | 4.78 | 26400 | 0.2051 | 0.1896 |
| 0.1901 | 4.85 | 26800 | 0.2059 | 0.1858 |
| 0.1934 | 4.92 | 27200 | 0.2028 | 0.1832 |
| 0.191 | 4.99 | 27600 | 0.2046 | 0.1870 |
| 0.1775 | 5.07 | 28000 | 0.2081 | 0.1891 |
| 0.175 | 5.14 | 28400 | 0.2084 | 0.1904 |
| 0.19 | 5.21 | 28800 | 0.2086 | 0.1920 |
| 0.1798 | 5.28 | 29200 | 0.2079 | 0.1935 |
| 0.1765 | 5.35 | 29600 | 0.2145 | 0.1930 |
| 0.181 | 5.43 | 30000 | 0.2062 | 0.1918 |
| 0.1808 | 5.5 | 30400 | 0.2083 | 0.1875 |
| 0.1769 | 5.57 | 30800 | 0.2117 | 0.1895 |
| 0.1788 | 5.64 | 31200 | 0.2055 | 0.1857 |
| 0.181 | 5.72 | 31600 | 0.2057 | 0.1870 |
| 0.1781 | 5.79 | 32000 | 0.2053 | 0.1872 |
| 0.1852 | 5.86 | 32400 | 0.2077 | 0.1904 |
| 0.1832 | 5.93 | 32800 | 0.1979 | 0.1821 |
| 0.1758 | 6.01 | 33200 | 0.1957 | 0.1754 |
| 0.1611 | 6.08 | 33600 | 0.2028 | 0.1773 |
| 0.1606 | 6.15 | 34000 | 0.2018 | 0.1780 |
| 0.1702 | 6.22 | 34400 | 0.1977 | 0.1759 |
| 0.1649 | 6.3 | 34800 | 0.2073 | 0.1845 |
| 0.1641 | 6.37 | 35200 | 0.1947 | 0.1774 |
| 0.1703 | 6.44 | 35600 | 0.2009 | 0.1811 |
| 0.1716 | 6.51 | 36000 | 0.2091 | 0.1817 |
| 0.1732 | 6.58 | 36400 | 0.1942 | 0.1743 |
| 0.1642 | 6.66 | 36800 | 0.1930 | 0.1749 |
| 0.1685 | 6.73 | 37200 | 0.1962 | 0.1716 |
| 0.1647 | 6.8 | 37600 | 0.1977 | 0.1822 |
| 0.1647 | 6.87 | 38000 | 0.1917 | 0.1748 |
| 0.1667 | 6.95 | 38400 | 0.1948 | 0.1774 |
| 0.1647 | 7.02 | 38800 | 0.2018 | 0.1783 |
| 0.15 | 7.09 | 39200 | 0.2010 | 0.1796 |
| 0.1663 | 7.16 | 39600 | 0.1969 | 0.1731 |
| 0.1536 | 7.24 | 40000 | 0.1935 | 0.1726 |
| 0.1544 | 7.31 | 40400 | 0.2030 | 0.1799 |
| 0.1536 | 7.38 | 40800 | 0.1973 | 0.1772 |
| 0.1559 | 7.45 | 41200 | 0.1973 | 0.1763 |
| 0.1547 | 7.53 | 41600 | 0.2052 | 0.1782 |
| 0.1584 | 7.6 | 42000 | 0.1965 | 0.1737 |
| 0.1542 | 7.67 | 42400 | 0.1878 | 0.1725 |
| 0.1525 | 7.74 | 42800 | 0.1946 | 0.1750 |
| 0.1547 | 7.81 | 43200 | 0.1934 | 0.1691 |
| 0.1534 | 7.89 | 43600 | 0.1919 | 0.1711 |
| 0.1574 | 7.96 | 44000 | 0.1935 | 0.1745 |
| 0.1471 | 8.03 | 44400 | 0.1915 | 0.1689 |
| 0.1433 | 8.1 | 44800 | 0.1956 | 0.1719 |
| 0.1433 | 8.18 | 45200 | 0.1980 | 0.1720 |
| 0.1424 | 8.25 | 45600 | 0.1906 | 0.1681 |
| 0.1428 | 8.32 | 46000 | 0.1892 | 0.1649 |
| 0.1424 | 8.39 | 46400 | 0.1916 | 0.1698 |
| 0.1466 | 8.47 | 46800 | 0.1970 | 0.1739 |
| 0.1496 | 8.54 | 47200 | 0.1902 | 0.1662 |
| 0.1408 | 8.61 | 47600 | 0.1858 | 0.1649 |
| 0.1445 | 8.68 | 48000 | 0.1893 | 0.1648 |
| 0.1459 | 8.76 | 48400 | 0.1875 | 0.1686 |
| 0.1433 | 8.83 | 48800 | 0.1920 | 0.1673 |
| 0.1448 | 8.9 | 49200 | 0.1833 | 0.1631 |
| 0.1461 | 8.97 | 49600 | 0.1904 | 0.1693 |
| 0.1451 | 9.04 | 50000 | 0.1969 | 0.1661 |
| 0.1336 | 9.12 | 50400 | 0.1950 | 0.1674 |
| 0.1362 | 9.19 | 50800 | 0.1971 | 0.1685 |
| 0.1316 | 9.26 | 51200 | 0.1928 | 0.1648 |
| 0.132 | 9.33 | 51600 | 0.1908 | 0.1615 |
| 0.1301 | 9.41 | 52000 | 0.1842 | 0.1569 |
| 0.1322 | 9.48 | 52400 | 0.1892 | 0.1616 |
| 0.1391 | 9.55 | 52800 | 0.1956 | 0.1656 |
| 0.132 | 9.62 | 53200 | 0.1876 | 0.1598 |
| 0.1349 | 9.7 | 53600 | 0.1870 | 0.1624 |
| 0.1325 | 9.77 | 54000 | 0.1834 | 0.1586 |
| 0.1389 | 9.84 | 54400 | 0.1892 | 0.1647 |
| 0.1364 | 9.91 | 54800 | 0.1840 | 0.1597 |
| 0.1339 | 9.99 | 55200 | 0.1858 | 0.1626 |
| 0.1269 | 10.06 | 55600 | 0.1875 | 0.1619 |
| 0.1229 | 10.13 | 56000 | 0.1909 | 0.1619 |
| 0.1258 | 10.2 | 56400 | 0.1933 | 0.1631 |
| 0.1256 | 10.27 | 56800 | 0.1930 | 0.1640 |
| 0.1207 | 10.35 | 57200 | 0.1823 | 0.1585 |
| 0.1248 | 10.42 | 57600 | 0.1889 | 0.1596 |
| 0.1264 | 10.49 | 58000 | 0.1845 | 0.1584 |
| 0.1251 | 10.56 | 58400 | 0.1869 | 0.1588 |
| 0.1251 | 10.64 | 58800 | 0.1885 | 0.1613 |
| 0.1276 | 10.71 | 59200 | 0.1855 | 0.1575 |
| 0.1303 | 10.78 | 59600 | 0.1836 | 0.1597 |
| 0.1246 | 10.85 | 60000 | 0.1810 | 0.1573 |
| 0.1283 | 10.93 | 60400 | 0.1830 | 0.1581 |
| 0.1273 | 11.0 | 60800 | 0.1837 | 0.1619 |
| 0.1202 | 11.07 | 61200 | 0.1865 | 0.1588 |
| 0.119 | 11.14 | 61600 | 0.1889 | 0.1580 |
| 0.1179 | 11.22 | 62000 | 0.1884 | 0.1592 |
| 0.1187 | 11.29 | 62400 | 0.1824 | 0.1565 |
| 0.1198 | 11.36 | 62800 | 0.1848 | 0.1552 |
| 0.1154 | 11.43 | 63200 | 0.1866 | 0.1565 |
| 0.1211 | 11.51 | 63600 | 0.1862 | 0.1563 |
| 0.1177 | 11.58 | 64000 | 0.1816 | 0.1527 |
| 0.1156 | 11.65 | 64400 | 0.1834 | 0.1540 |
| 0.1144 | 11.72 | 64800 | 0.1837 | 0.1524 |
| 0.119 | 11.79 | 65200 | 0.1859 | 0.1538 |
| 0.1183 | 11.87 | 65600 | 0.1869 | 0.1558 |
| 0.122 | 11.94 | 66000 | 0.1853 | 0.1535 |
| 0.1197 | 12.01 | 66400 | 0.1871 | 0.1586 |
| 0.1096 | 12.08 | 66800 | 0.1838 | 0.1540 |
| 0.1074 | 12.16 | 67200 | 0.1915 | 0.1592 |
| 0.1084 | 12.23 | 67600 | 0.1845 | 0.1545 |
| 0.1097 | 12.3 | 68000 | 0.1904 | 0.1552 |
| 0.112 | 12.37 | 68400 | 0.1846 | 0.1578 |
| 0.1109 | 12.45 | 68800 | 0.1862 | 0.1549 |
| 0.1114 | 12.52 | 69200 | 0.1889 | 0.1552 |
| 0.1119 | 12.59 | 69600 | 0.1828 | 0.1530 |
| 0.1124 | 12.66 | 70000 | 0.1822 | 0.1540 |
| 0.1127 | 12.74 | 70400 | 0.1865 | 0.1589 |
| 0.1128 | 12.81 | 70800 | 0.1786 | 0.1498 |
| 0.1069 | 12.88 | 71200 | 0.1813 | 0.1522 |
| 0.1069 | 12.95 | 71600 | 0.1895 | 0.1558 |
| 0.1083 | 13.02 | 72000 | 0.1925 | 0.1557 |
| 0.1009 | 13.1 | 72400 | 0.1883 | 0.1522 |
| 0.1007 | 13.17 | 72800 | 0.1829 | 0.1480 |
| 0.1014 | 13.24 | 73200 | 0.1861 | 0.1510 |
| 0.0974 | 13.31 | 73600 | 0.1836 | 0.1486 |
| 0.1006 | 13.39 | 74000 | 0.1821 | 0.1462 |
| 0.0973 | 13.46 | 74400 | 0.1857 | 0.1484 |
| 0.1011 | 13.53 | 74800 | 0.1822 | 0.1471 |
| 0.1031 | 13.6 | 75200 | 0.1823 | 0.1489 |
| 0.1034 | 13.68 | 75600 | 0.1809 | 0.1452 |
| 0.0998 | 13.75 | 76000 | 0.1817 | 0.1490 |
| 0.1071 | 13.82 | 76400 | 0.1808 | 0.1501 |
| 0.1083 | 13.89 | 76800 | 0.1796 | 0.1475 |
| 0.1053 | 13.97 | 77200 | 0.1785 | 0.1470 |
| 0.0978 | 14.04 | 77600 | 0.1886 | 0.1495 |
| 0.094 | 14.11 | 78000 | 0.1854 | 0.1489 |
| 0.0915 | 14.18 | 78400 | 0.1854 | 0.1498 |
| 0.0947 | 14.25 | 78800 | 0.1888 | 0.1500 |
| 0.0939 | 14.33 | 79200 | 0.1885 | 0.1494 |
| 0.0973 | 14.4 | 79600 | 0.1877 | 0.1466 |
| 0.0946 | 14.47 | 80000 | 0.1904 | 0.1494 |
| 0.0931 | 14.54 | 80400 | 0.1815 | 0.1473 |
| 0.0958 | 14.62 | 80800 | 0.1905 | 0.1508 |
| 0.0982 | 14.69 | 81200 | 0.1881 | 0.1511 |
| 0.0963 | 14.76 | 81600 | 0.1823 | 0.1449 |
| 0.0943 | 14.83 | 82000 | 0.1782 | 0.1458 |
| 0.0981 | 14.91 | 82400 | 0.1795 | 0.1465 |
| 0.0995 | 14.98 | 82800 | 0.1811 | 0.1484 |
| 0.0909 | 15.05 | 83200 | 0.1822 | 0.1450 |
| 0.0872 | 15.12 | 83600 | 0.1890 | 0.1466 |
| 0.0878 | 15.2 | 84000 | 0.1859 | 0.1468 |
| 0.0884 | 15.27 | 84400 | 0.1825 | 0.1429 |
| 0.0871 | 15.34 | 84800 | 0.1816 | 0.1438 |
| 0.0883 | 15.41 | 85200 | 0.1817 | 0.1433 |
| 0.0844 | 15.48 | 85600 | 0.1821 | 0.1412 |
| 0.0843 | 15.56 | 86000 | 0.1863 | 0.1411 |
| 0.0805 | 15.63 | 86400 | 0.1863 | 0.1441 |
| 0.085 | 15.7 | 86800 | 0.1808 | 0.1440 |
| 0.0848 | 15.77 | 87200 | 0.1808 | 0.1421 |
| 0.0844 | 15.85 | 87600 | 0.1841 | 0.1406 |
| 0.082 | 15.92 | 88000 | 0.1850 | 0.1442 |
| 0.0854 | 15.99 | 88400 | 0.1773 | 0.1426 |
| 0.0835 | 16.06 | 88800 | 0.1888 | 0.1436 |
| 0.0789 | 16.14 | 89200 | 0.1922 | 0.1434 |
| 0.081 | 16.21 | 89600 | 0.1864 | 0.1448 |
| 0.0799 | 16.28 | 90000 | 0.1902 | 0.1428 |
| 0.0848 | 16.35 | 90400 | 0.1873 | 0.1422 |
| 0.084 | 16.43 | 90800 | 0.1835 | 0.1421 |
| 0.083 | 16.5 | 91200 | 0.1878 | 0.1390 |
| 0.0794 | 16.57 | 91600 | 0.1877 | 0.1398 |
| 0.0807 | 16.64 | 92000 | 0.1800 | 0.1385 |
| 0.0829 | 16.71 | 92400 | 0.1910 | 0.1434 |
| 0.0839 | 16.79 | 92800 | 0.1843 | 0.1381 |
| 0.0815 | 16.86 | 93200 | 0.1812 | 0.1365 |
| 0.0831 | 16.93 | 93600 | 0.1889 | 0.1383 |
| 0.0803 | 17.0 | 94000 | 0.1902 | 0.1403 |
| 0.0724 | 17.08 | 94400 | 0.1934 | 0.1380 |
| 0.0734 | 17.15 | 94800 | 0.1865 | 0.1394 |
| 0.0739 | 17.22 | 95200 | 0.1876 | 0.1395 |
| 0.0758 | 17.29 | 95600 | 0.1938 | 0.1411 |
| 0.0733 | 17.37 | 96000 | 0.1933 | 0.1410 |
| 0.077 | 17.44 | 96400 | 0.1848 | 0.1385 |
| 0.0754 | 17.51 | 96800 | 0.1876 | 0.1407 |
| 0.0746 | 17.58 | 97200 | 0.1863 | 0.1371 |
| 0.0732 | 17.66 | 97600 | 0.1927 | 0.1401 |
| 0.0746 | 17.73 | 98000 | 0.1874 | 0.1390 |
| 0.0755 | 17.8 | 98400 | 0.1853 | 0.1381 |
| 0.0724 | 17.87 | 98800 | 0.1849 | 0.1365 |
| 0.0716 | 17.94 | 99200 | 0.1848 | 0.1380 |
| 0.074 | 18.02 | 99600 | 0.1891 | 0.1362 |
| 0.0687 | 18.09 | 100000 | 0.1974 | 0.1357 |
| 0.0651 | 18.16 | 100400 | 0.1942 | 0.1353 |
| 0.0672 | 18.23 | 100800 | 0.1823 | 0.1363 |
| 0.0671 | 18.31 | 101200 | 0.1959 | 0.1357 |
| 0.0684 | 18.38 | 101600 | 0.1959 | 0.1374 |
| 0.0688 | 18.45 | 102000 | 0.1904 | 0.1353 |
| 0.0696 | 18.52 | 102400 | 0.1926 | 0.1364 |
| 0.0661 | 18.6 | 102800 | 0.1905 | 0.1351 |
| 0.0684 | 18.67 | 103200 | 0.1955 | 0.1343 |
| 0.0712 | 18.74 | 103600 | 0.1873 | 0.1353 |
| 0.0701 | 18.81 | 104000 | 0.1822 | 0.1354 |
| 0.0688 | 18.89 | 104400 | 0.1905 | 0.1373 |
| 0.0695 | 18.96 | 104800 | 0.1879 | 0.1335 |
| 0.0661 | 19.03 | 105200 | 0.2005 | 0.1351 |
| 0.0644 | 19.1 | 105600 | 0.1972 | 0.1351 |
| 0.0627 | 19.18 | 106000 | 0.1956 | 0.1340 |
| 0.0633 | 19.25 | 106400 | 0.1962 | 0.1340 |
| 0.0629 | 19.32 | 106800 | 0.1937 | 0.1342 |
| 0.0636 | 19.39 | 107200 | 0.1905 | 0.1355 |
| 0.0631 | 19.46 | 107600 | 0.1917 | 0.1326 |
| 0.0624 | 19.54 | 108000 | 0.1977 | 0.1355 |
| 0.0621 | 19.61 | 108400 | 0.1941 | 0.1345 |
| 0.0635 | 19.68 | 108800 | 0.1949 | 0.1336 |
| 0.063 | 19.75 | 109200 | 0.1919 | 0.1317 |
| 0.0636 | 19.83 | 109600 | 0.1928 | 0.1317 |
| 0.0612 | 19.9 | 110000 | 0.1923 | 0.1314 |
| 0.0636 | 19.97 | 110400 | 0.1923 | 0.1343 |
| 0.0581 | 20.04 | 110800 | 0.2036 | 0.1332 |
| 0.0573 | 20.12 | 111200 | 0.2007 | 0.1315 |
| 0.0566 | 20.19 | 111600 | 0.1974 | 0.1319 |
| 0.0589 | 20.26 | 112000 | 0.1958 | 0.1322 |
| 0.0577 | 20.33 | 112400 | 0.1946 | 0.1307 |
| 0.0587 | 20.41 | 112800 | 0.1957 | 0.1295 |
| 0.0588 | 20.48 | 113200 | 0.2013 | 0.1306 |
| 0.0594 | 20.55 | 113600 | 0.2010 | 0.1312 |
| 0.0602 | 20.62 | 114000 | 0.1993 | 0.1314 |
| 0.0583 | 20.69 | 114400 | 0.1931 | 0.1297 |
| 0.059 | 20.77 | 114800 | 0.1974 | 0.1305 |
| 0.0566 | 20.84 | 115200 | 0.1979 | 0.1294 |
| 0.0588 | 20.91 | 115600 | 0.1944 | 0.1292 |
| 0.0569 | 20.98 | 116000 | 0.1974 | 0.1309 |
| 0.0554 | 21.06 | 116400 | 0.2080 | 0.1307 |
| 0.0542 | 21.13 | 116800 | 0.2056 | 0.1301 |
| 0.0532 | 21.2 | 117200 | 0.2027 | 0.1309 |
| 0.0535 | 21.27 | 117600 | 0.1970 | 0.1287 |
| 0.0533 | 21.35 | 118000 | 0.2124 | 0.1310 |
| 0.0546 | 21.42 | 118400 | 0.2043 | 0.1300 |
| 0.0544 | 21.49 | 118800 | 0.2056 | 0.1281 |
| 0.0562 | 21.56 | 119200 | 0.1986 | 0.1273 |
| 0.0549 | 21.64 | 119600 | 0.2075 | 0.1283 |
| 0.0522 | 21.71 | 120000 | 0.2058 | 0.1278 |
| 0.052 | 21.78 | 120400 | 0.2057 | 0.1280 |
| 0.0563 | 21.85 | 120800 | 0.1966 | 0.1295 |
| 0.0546 | 21.92 | 121200 | 0.2002 | 0.1285 |
| 0.0539 | 22.0 | 121600 | 0.1996 | 0.1279 |
| 0.0504 | 22.07 | 122000 | 0.2077 | 0.1273 |
| 0.0602 | 22.14 | 122400 | 0.2055 | 0.1278 |
| 0.0503 | 22.21 | 122800 | 0.2037 | 0.1283 |
| 0.0496 | 22.29 | 123200 | 0.2109 | 0.1279 |
| 0.0523 | 22.36 | 123600 | 0.2068 | 0.1276 |
| 0.0508 | 22.43 | 124000 | 0.2051 | 0.1257 |
| 0.0505 | 22.5 | 124400 | 0.2056 | 0.1269 |
| 0.05 | 22.58 | 124800 | 0.1995 | 0.1268 |
| 0.0496 | 22.65 | 125200 | 0.2022 | 0.1290 |
| 0.0484 | 22.72 | 125600 | 0.2095 | 0.1291 |
| 0.0518 | 22.79 | 126000 | 0.2132 | 0.1271 |
| 0.0499 | 22.87 | 126400 | 0.2124 | 0.1263 |
| 0.0485 | 22.94 | 126800 | 0.2092 | 0.1252 |
| 0.0476 | 23.01 | 127200 | 0.2138 | 0.1256 |
| 0.0467 | 23.08 | 127600 | 0.2119 | 0.1256 |
| 0.048 | 23.15 | 128000 | 0.2138 | 0.1269 |
| 0.0461 | 23.23 | 128400 | 0.2036 | 0.1244 |
| 0.0467 | 23.3 | 128800 | 0.2163 | 0.1255 |
| 0.0475 | 23.37 | 129200 | 0.2180 | 0.1258 |
| 0.0468 | 23.44 | 129600 | 0.2129 | 0.1245 |
| 0.0456 | 23.52 | 130000 | 0.2122 | 0.1250 |
| 0.0458 | 23.59 | 130400 | 0.2157 | 0.1257 |
| 0.0453 | 23.66 | 130800 | 0.2088 | 0.1242 |
| 0.045 | 23.73 | 131200 | 0.2144 | 0.1247 |
| 0.0469 | 23.81 | 131600 | 0.2113 | 0.1246 |
| 0.0453 | 23.88 | 132000 | 0.2151 | 0.1234 |
| 0.0471 | 23.95 | 132400 | 0.2130 | 0.1229 |
| 0.0443 | 24.02 | 132800 | 0.2150 | 0.1225 |
| 0.0446 | 24.1 | 133200 | 0.2166 | 0.1235 |
| 0.0435 | 24.17 | 133600 | 0.2143 | 0.1222 |
| 0.0407 | 24.24 | 134000 | 0.2175 | 0.1218 |
| 0.0421 | 24.31 | 134400 | 0.2147 | 0.1227 |
| 0.0435 | 24.38 | 134800 | 0.2193 | 0.1233 |
| 0.0414 | 24.46 | 135200 | 0.2172 | 0.1225 |
| 0.0419 | 24.53 | 135600 | 0.2156 | 0.1225 |
| 0.0419 | 24.6 | 136000 | 0.2143 | 0.1235 |
| 0.0423 | 24.67 | 136400 | 0.2179 | 0.1226 |
| 0.0423 | 24.75 | 136800 | 0.2144 | 0.1221 |
| 0.0424 | 24.82 | 137200 | 0.2135 | 0.1210 |
| 0.0419 | 24.89 | 137600 | 0.2166 | 0.1218 |
| 0.0408 | 24.96 | 138000 | 0.2151 | 0.1211 |
| 0.0433 | 25.04 | 138400 | 0.2174 | 0.1214 |
| 0.0395 | 25.11 | 138800 | 0.2242 | 0.1210 |
| 0.0403 | 25.18 | 139200 | 0.2219 | 0.1215 |
| 0.0413 | 25.25 | 139600 | 0.2225 | 0.1207 |
| 0.0389 | 25.33 | 140000 | 0.2187 | 0.1202 |
| 0.0395 | 25.4 | 140400 | 0.2244 | 0.1204 |
| 0.0398 | 25.47 | 140800 | 0.2263 | 0.1199 |
| 0.0386 | 25.54 | 141200 | 0.2165 | 0.1187 |
| 0.0396 | 25.61 | 141600 | 0.2171 | 0.1187 |
| 0.0406 | 25.69 | 142000 | 0.2199 | 0.1190 |
| 0.0404 | 25.76 | 142400 | 0.2224 | 0.1190 |
| 0.0391 | 25.83 | 142800 | 0.2230 | 0.1185 |
| 0.04 | 25.9 | 143200 | 0.2208 | 0.1200 |
| 0.0396 | 25.98 | 143600 | 0.2179 | 0.1191 |
| 0.0353 | 26.05 | 144000 | 0.2285 | 0.1178 |
| 0.0368 | 26.12 | 144400 | 0.2273 | 0.1186 |
| 0.0393 | 26.19 | 144800 | 0.2247 | 0.1196 |
| 0.0368 | 26.27 | 145200 | 0.2314 | 0.1181 |
| 0.0373 | 26.34 | 145600 | 0.2215 | 0.1188 |
| 0.038 | 26.41 | 146000 | 0.2262 | 0.1180 |
| 0.0363 | 26.48 | 146400 | 0.2250 | 0.1172 |
| 0.0365 | 26.56 | 146800 | 0.2299 | 0.1174 |
| 0.0382 | 26.63 | 147200 | 0.2292 | 0.1165 |
| 0.0365 | 26.7 | 147600 | 0.2282 | 0.1165 |
| 0.0371 | 26.77 | 148000 | 0.2276 | 0.1172 |
| 0.0365 | 26.85 | 148400 | 0.2280 | 0.1173 |
| 0.0376 | 26.92 | 148800 | 0.2248 | 0.1164 |
| 0.0365 | 26.99 | 149200 | 0.2230 | 0.1158 |
| 0.0343 | 27.06 | 149600 | 0.2300 | 0.1157 |
| 0.0354 | 27.13 | 150000 | 0.2298 | 0.1166 |
| 0.0333 | 27.21 | 150400 | 0.2307 | 0.1158 |
| 0.0353 | 27.28 | 150800 | 0.2300 | 0.1157 |
| 0.036 | 27.35 | 151200 | 0.2335 | 0.1160 |
| 0.0343 | 27.42 | 151600 | 0.2324 | 0.1155 |
| 0.0361 | 27.5 | 152000 | 0.2300 | 0.1150 |
| 0.0352 | 27.57 | 152400 | 0.2279 | 0.1146 |
| 0.0353 | 27.64 | 152800 | 0.2307 | 0.1149 |
| 0.0342 | 27.71 | 153200 | 0.2315 | 0.1152 |
| 0.0345 | 27.79 | 153600 | 0.2290 | 0.1146 |
| 0.034 | 27.86 | 154000 | 0.2319 | 0.1141 |
| 0.0347 | 27.93 | 154400 | 0.2312 | 0.1144 |
| 0.0338 | 28.0 | 154800 | 0.2328 | 0.1146 |
| 0.0347 | 28.08 | 155200 | 0.2352 | 0.1151 |
| 0.033 | 28.15 | 155600 | 0.2337 | 0.1142 |
| 0.0336 | 28.22 | 156000 | 0.2345 | 0.1141 |
| 0.0337 | 28.29 | 156400 | 0.2315 | 0.1143 |
| 0.0314 | 28.36 | 156800 | 0.2353 | 0.1140 |
| 0.0333 | 28.44 | 157200 | 0.2338 | 0.1146 |
| 0.0317 | 28.51 | 157600 | 0.2345 | 0.1139 |
| 0.0326 | 28.58 | 158000 | 0.2336 | 0.1143 |
| 0.033 | 28.65 | 158400 | 0.2352 | 0.1137 |
| 0.0325 | 28.73 | 158800 | 0.2312 | 0.1130 |
| 0.0321 | 28.8 | 159200 | 0.2338 | 0.1133 |
| 0.0334 | 28.87 | 159600 | 0.2335 | 0.1130 |
| 0.0317 | 28.94 | 160000 | 0.2340 | 0.1126 |
| 0.0321 | 29.02 | 160400 | 0.2349 | 0.1126 |
| 0.032 | 29.09 | 160800 | 0.2369 | 0.1127 |
| 0.0312 | 29.16 | 161200 | 0.2363 | 0.1124 |
| 0.0303 | 29.23 | 161600 | 0.2363 | 0.1123 |
| 0.0322 | 29.31 | 162000 | 0.2354 | 0.1124 |
| 0.03 | 29.38 | 162400 | 0.2360 | 0.1122 |
| 0.0299 | 29.45 | 162800 | 0.2378 | 0.1124 |
| 0.0313 | 29.52 | 163200 | 0.2377 | 0.1120 |
| 0.0299 | 29.59 | 163600 | 0.2367 | 0.1124 |
| 0.0313 | 29.67 | 164000 | 0.2380 | 0.1120 |
| 0.031 | 29.74 | 164400 | 0.2369 | 0.1120 |
| 0.0327 | 29.81 | 164800 | 0.2358 | 0.1117 |
| 0.0316 | 29.88 | 165200 | 0.2358 | 0.1118 |
| 0.0307 | 29.96 | 165600 | 0.2362 | 0.1118 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
yaswanth/xls-r-300m-yaswanth-hindi2
|
yaswanth
| 2022-03-23T18:28:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: xls-r-300m-yaswanth-hindi2
results: []
---
<!-- 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. -->
# xls-r-300m-yaswanth-hindi2
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: 1.7163
- Wer: 0.6951
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0007
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.986 | 4.46 | 500 | 2.0194 | 1.1857 |
| 0.9232 | 8.93 | 1000 | 1.2665 | 0.8435 |
| 0.5094 | 13.39 | 1500 | 1.2473 | 0.7893 |
| 0.3618 | 17.86 | 2000 | 1.3675 | 0.7789 |
| 0.2914 | 22.32 | 2500 | 1.3725 | 0.7914 |
| 0.2462 | 26.79 | 3000 | 1.4567 | 0.7795 |
| 0.228 | 31.25 | 3500 | 1.6179 | 0.7872 |
| 0.1995 | 35.71 | 4000 | 1.4932 | 0.7555 |
| 0.1878 | 40.18 | 4500 | 1.5352 | 0.7480 |
| 0.165 | 44.64 | 5000 | 1.5238 | 0.7440 |
| 0.1514 | 49.11 | 5500 | 1.5842 | 0.7498 |
| 0.1416 | 53.57 | 6000 | 1.6662 | 0.7524 |
| 0.1351 | 58.04 | 6500 | 1.6280 | 0.7356 |
| 0.1196 | 62.5 | 7000 | 1.6329 | 0.7250 |
| 0.1109 | 66.96 | 7500 | 1.6435 | 0.7302 |
| 0.1008 | 71.43 | 8000 | 1.7058 | 0.7170 |
| 0.0907 | 75.89 | 8500 | 1.6880 | 0.7387 |
| 0.0816 | 80.36 | 9000 | 1.6957 | 0.7031 |
| 0.0743 | 84.82 | 9500 | 1.7547 | 0.7222 |
| 0.0694 | 89.29 | 10000 | 1.6974 | 0.7117 |
| 0.0612 | 93.75 | 10500 | 1.7251 | 0.7020 |
| 0.0577 | 98.21 | 11000 | 1.7163 | 0.6951 |
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
geninhu/xls-asr-vi-40h-1B
|
geninhu
| 2022-03-23T18:27:57Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"robust-speech-event",
"vi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- vi
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: xls-asr-vi-40h-1B
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: vi
metrics:
- name: Test WER (with LM)
type: wer
value: 25.846
- name: Test CER (with LM)
type: cer
value: 12.961
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: vi
metrics:
- name: Test WER (with LM)
type: wer
value: 31.158
- name: Test CER (with LM)
type: cer
value: 16.179
---
# xls-asr-vi-40h-1B
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on 40 hours of FPT Open Speech Dataset (FOSD) and Common Voice 7.0.
### Benchmark WER result:
| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|---|
|without LM| 25.93 | 34.21 |
|with 4-grams LM| 24.11 | 25.84 | 31.158 |
### Benchmark CER result:
| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|---|
|without LM| 9.24 | 19.94 |
|with 4-grams LM| 10.37 | 12.96 | 16.179 |
## Evaluation
Please use the eval.py file to run the evaluation
```python
python eval.py --model_id geninhu/xls-asr-vi-40h-1B --dataset mozilla-foundation/common_voice_7_0 --config vi --split test --log_outputs
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.6222 | 1.85 | 1500 | 5.9479 | 0.5474 |
| 1.1362 | 3.7 | 3000 | 7.9799 | 0.5094 |
| 0.7814 | 5.56 | 4500 | 5.0330 | 0.4724 |
| 0.6281 | 7.41 | 6000 | 2.3484 | 0.5020 |
| 0.5472 | 9.26 | 7500 | 2.2495 | 0.4793 |
| 0.4827 | 11.11 | 9000 | 1.1530 | 0.4768 |
| 0.4327 | 12.96 | 10500 | 1.6160 | 0.4646 |
| 0.3989 | 14.81 | 12000 | 3.2633 | 0.4703 |
| 0.3522 | 16.67 | 13500 | 2.2337 | 0.4708 |
| 0.3201 | 18.52 | 15000 | 3.6879 | 0.4565 |
| 0.2899 | 20.37 | 16500 | 5.4389 | 0.4599 |
| 0.2776 | 22.22 | 18000 | 3.5284 | 0.4537 |
| 0.2574 | 24.07 | 19500 | 2.1759 | 0.4649 |
| 0.2378 | 25.93 | 21000 | 3.3901 | 0.4448 |
| 0.217 | 27.78 | 22500 | 1.1632 | 0.4565 |
| 0.2115 | 29.63 | 24000 | 1.7441 | 0.4232 |
| 0.1959 | 31.48 | 25500 | 3.4992 | 0.4304 |
| 0.187 | 33.33 | 27000 | 3.6163 | 0.4369 |
| 0.1748 | 35.19 | 28500 | 3.6038 | 0.4467 |
| 0.17 | 37.04 | 30000 | 2.9708 | 0.4362 |
| 0.159 | 38.89 | 31500 | 3.2045 | 0.4279 |
| 0.153 | 40.74 | 33000 | 3.2427 | 0.4287 |
| 0.1463 | 42.59 | 34500 | 3.5439 | 0.4270 |
| 0.139 | 44.44 | 36000 | 3.9381 | 0.4150 |
| 0.1352 | 46.3 | 37500 | 4.1744 | 0.4092 |
| 0.1369 | 48.15 | 39000 | 4.2279 | 0.4154 |
| 0.1273 | 50.0 | 40500 | 4.1691 | 0.4133 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
nouamanetazi/wav2vec2-xls-r-300m-ar-with-lm
|
nouamanetazi
| 2022-03-23T18:27:54Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ar
license: apache-2.0
tags:
- ar
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: XLS-R-300M - Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 1.0
- name: Test CER
type: cer
value: 1.0
---
<!-- 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-ar
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 - AR dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.0191
- eval_wer: 1.0
- eval_runtime: 252.2389
- eval_samples_per_second: 30.217
- eval_steps_per_second: 0.476
- epoch: 1.0
- step: 340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
Please use the evaluation script `eval.py` included in the repo.
1. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id nouamanetazi/wav2vec2-xls-r-300m-ar --dataset speech-recognition-community-v2/dev_data --config ar --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
|
arijitx/wav2vec2-xls-r-300m-bengali
|
arijitx
| 2022-03-23T18:27:52Z | 427 | 6 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"bn",
"hf-asr-leaderboard",
"openslr_SLR53",
"robust-speech-event",
"dataset:openslr",
"dataset:SLR53",
"dataset:AI4Bharat/IndicCorp",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bn
license: apache-2.0
tags:
- automatic-speech-recognition
- bn
- hf-asr-leaderboard
- openslr_SLR53
- robust-speech-event
datasets:
- openslr
- SLR53
- AI4Bharat/IndicCorp
metrics:
- wer
- cer
model-index:
- name: arijitx/wav2vec2-xls-r-300m-bengali
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: openslr
name: Open SLR
args: SLR53
metrics:
- type: wer
value: 0.21726385291857586
name: Test WER
- type: cer
value: 0.04725010353701041
name: Test CER
- type: wer
value: 0.15322879016421437
name: Test WER with lm
- type: cer
value: 0.03413696666806267
name: Test CER with lm
---
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set.
Without language model :
- WER: 0.21726385291857586
- CER: 0.04725010353701041
With 5 gram language model trained on 30M sentences randomly chosen from [AI4Bharat IndicCorp](https://indicnlp.ai4bharat.org/corpora/) dataset :
- WER: 0.15322879016421437
- CER: 0.03413696666806267
Note : 5% of a total 10935 samples have been used for evaluation. Evaluation set has 10935 examples which was not part of training training was done on first 95% and eval was done on last 5%. Training was stopped after 180k steps. Output predictions are available under files section.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset_name="openslr"
- model_name_or_path="facebook/wav2vec2-xls-r-300m"
- dataset_config_name="SLR53"
- output_dir="./wav2vec2-xls-r-300m-bengali"
- overwrite_output_dir
- num_train_epochs="50"
- per_device_train_batch_size="32"
- per_device_eval_batch_size="32"
- gradient_accumulation_steps="1"
- learning_rate="7.5e-5"
- warmup_steps="2000"
- length_column_name="input_length"
- evaluation_strategy="steps"
- text_column_name="sentence"
- chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … –
- save_steps="2000"
- eval_steps="3000"
- logging_steps="100"
- layerdrop="0.0"
- activation_dropout="0.1"
- save_total_limit="3"
- freeze_feature_encoder
- feat_proj_dropout="0.0"
- mask_time_prob="0.75"
- mask_time_length="10"
- mask_feature_prob="0.25"
- mask_feature_length="64"
- preprocessing_num_workers 32
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
Notes
- Training and eval code modified from : https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event.
- Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used.
- Minimum audio duration of 0.5s has been used to filter the training data which excluded may be 10-20 samples.
- OpenSLR53 transcripts are *not* part of LM training and LM used to evaluate.
|
pablouribe/xls-r-spanish-test
|
pablouribe
| 2022-03-23T18:27:46Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: xls-r-spanish-test
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: es
metrics:
- name: Test WER
type: wer
value: 13.89
- name: Test CER
type: cer
value: 3.85
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: es
metrics:
- name: Test WER
type: wer
value: 37.66
- name: Test CER
type: cer
value: 15.32
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: es
metrics:
- name: Test WER
type: wer
value: 41.17
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ES dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1461
- Wer: 1.0063
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.953 | 0.15 | 1000 | 2.9528 | 1.0 |
| 1.1519 | 0.3 | 2000 | 0.3735 | 1.0357 |
| 1.0278 | 0.45 | 3000 | 0.2529 | 1.0390 |
| 0.9922 | 0.61 | 4000 | 0.2208 | 1.0270 |
| 0.9618 | 0.76 | 5000 | 0.2088 | 1.0294 |
| 0.9364 | 0.91 | 6000 | 0.2019 | 1.0214 |
| 0.9179 | 1.06 | 7000 | 0.1940 | 1.0294 |
| 0.9154 | 1.21 | 8000 | 0.1915 | 1.0290 |
| 0.8985 | 1.36 | 9000 | 0.1837 | 1.0211 |
| 0.9055 | 1.51 | 10000 | 0.1838 | 1.0273 |
| 0.8861 | 1.67 | 11000 | 0.1765 | 1.0139 |
| 0.892 | 1.82 | 12000 | 0.1723 | 1.0188 |
| 0.8778 | 1.97 | 13000 | 0.1735 | 1.0092 |
| 0.8645 | 2.12 | 14000 | 0.1707 | 1.0106 |
| 0.8595 | 2.27 | 15000 | 0.1713 | 1.0186 |
| 0.8392 | 2.42 | 16000 | 0.1686 | 1.0053 |
| 0.8436 | 2.57 | 17000 | 0.1653 | 1.0096 |
| 0.8405 | 2.73 | 18000 | 0.1689 | 1.0077 |
| 0.8382 | 2.88 | 19000 | 0.1645 | 1.0114 |
| 0.8247 | 3.03 | 20000 | 0.1647 | 1.0078 |
| 0.8219 | 3.18 | 21000 | 0.1611 | 1.0026 |
| 0.8024 | 3.33 | 22000 | 0.1580 | 1.0062 |
| 0.8087 | 3.48 | 23000 | 0.1578 | 1.0038 |
| 0.8097 | 3.63 | 24000 | 0.1556 | 1.0057 |
| 0.8094 | 3.79 | 25000 | 0.1552 | 1.0035 |
| 0.7836 | 3.94 | 26000 | 0.1516 | 1.0052 |
| 0.8042 | 4.09 | 27000 | 0.1515 | 1.0054 |
| 0.7925 | 4.24 | 28000 | 0.1499 | 1.0031 |
| 0.7855 | 4.39 | 29000 | 0.1490 | 1.0041 |
| 0.7814 | 4.54 | 30000 | 0.1482 | 1.0068 |
| 0.7859 | 4.69 | 31000 | 0.1460 | 1.0066 |
| 0.7819 | 4.85 | 32000 | 0.1464 | 1.0062 |
| 0.7784 | 5.0 | 33000 | 0.1460 | 1.0063 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
|
kapilkd13/xls-r-300m-hi-prod
|
kapilkd13
| 2022-03-23T18:27:33Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 39.21
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7805
- Wer: 0.4340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.36 | 400 | 1.9130 | 0.9244 |
| 5.0013 | 2.71 | 800 | 0.7789 | 0.5944 |
| 0.6544 | 4.07 | 1200 | 0.7298 | 0.5852 |
| 0.4021 | 5.42 | 1600 | 0.6978 | 0.5667 |
| 0.3003 | 6.78 | 2000 | 0.6764 | 0.5382 |
| 0.3003 | 8.14 | 2400 | 0.7249 | 0.5463 |
| 0.2345 | 9.49 | 2800 | 0.7280 | 0.5124 |
| 0.1993 | 10.85 | 3200 | 0.7289 | 0.4690 |
| 0.1617 | 12.2 | 3600 | 0.7431 | 0.4733 |
| 0.1432 | 13.56 | 4000 | 0.7448 | 0.4733 |
| 0.1432 | 14.92 | 4400 | 0.7746 | 0.4485 |
| 0.1172 | 16.27 | 4800 | 0.7589 | 0.4742 |
| 0.1035 | 17.63 | 5200 | 0.7539 | 0.4353 |
| 0.0956 | 18.98 | 5600 | 0.7648 | 0.4495 |
| 0.0845 | 20.34 | 6000 | 0.7877 | 0.4719 |
| 0.0845 | 21.69 | 6400 | 0.7884 | 0.4434 |
| 0.0761 | 23.05 | 6800 | 0.7796 | 0.4386 |
| 0.0634 | 24.41 | 7200 | 0.7729 | 0.4306 |
| 0.0571 | 25.76 | 7600 | 0.7826 | 0.4298 |
| 0.0508 | 27.12 | 8000 | 0.7805 | 0.4340 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
comodoro/wav2vec2-xls-r-300m-west-slavic-cv8
|
comodoro
| 2022-03-23T18:27:31Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"xlsr-fine-tuning-week",
"cs",
"hsb",
"pl",
"sk",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- cs
- hsb
- pl
- sk
- sl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- xlsr-fine-tuning-week
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-300m-west-slavic-cv8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: cs
metrics:
- name: Test WER
type: wer
value: 53.5
- name: Test CER
type: cer
value: 14.7
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hsb
metrics:
- name: Test WER
type: wer
value: 81.7
- name: Test CER
type: cer
value: 21.2
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pl
metrics:
- name: Test WER
type: wer
value: 60.2
- name: Test CER
type: cer
value: 15.6
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sk
metrics:
- name: Test WER
type: wer
value: 69.6
- name: Test CER
type: cer
value: 20.7
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sl
metrics:
- name: Test WER
type: wer
value: 73.2
- name: Test CER
type: cer
value: 23.2
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: cs
metrics:
- name: Test WER
type: wer
value: 84.11
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: cs
metrics:
- name: Test WER
type: wer
value: 75.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pl
metrics:
- name: Test WER
type: wer
value: 65.3
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pl
metrics:
- name: Test WER
type: wer
value: 72.0
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sk
metrics:
- name: Test WER
type: wer
value: 88.37
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sk
metrics:
- name: Test WER
type: wer
value: 89.08
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Test WER
type: wer
value: 87.69
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 87.89
---
# wav2vec2-xls-r-300m-west-slavic-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 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.
Evaluation set used for training was concatenated from the respective test sets and shuffled while limiting each language to at most 2000 samples. During training, cca WER 70 was achieved on this set.
### Evaluation script
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-west-slavic-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config {lang}
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
patrickvonplaten/xls-r-300-sv-cv7
|
patrickvonplaten
| 2022-03-23T18:27:10Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"sv",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
- sv
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Swedish - CV7 - v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: sv-SE
metrics:
- name: Test WER
type: wer
value: 15.99
- name: Test CER
type: cer
value: 5.2
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 24.41
- name: Test CER
type: cer
value: 11.88
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2604
- Wer: 0.2334
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 1
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
See Tensorboard
### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id patrickvonplaten/xls-r-300-sv-cv7 --dataset mozilla-foundation/common_voice_7_0 --config sv-SE --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id patrickvonplaten/xls-r-300-sv-cv7 --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.18.4.dev0
- Tokenizers 0.10.3
|
anuragshas/wav2vec2-large-xls-r-300m-bg
|
anuragshas
| 2022-03-23T18:26:55Z | 228 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"bg",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bg
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
model-index:
- name: XLS-R-300M - Bulgarian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bg
metrics:
- name: Test WER
type: wer
value: 21.195
- name: Test CER
type: cer
value: 4.786
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: bg
metrics:
- name: Test WER
type: wer
value: 32.667
- name: Test CER
type: cer
value: 12.452
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: bg
metrics:
- name: Test WER
type: wer
value: 31.03
---
<!-- 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. -->
# XLS-R-300M - Bulgarian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2473
- Wer: 0.3002
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1589 | 3.48 | 400 | 3.0830 | 1.0 |
| 2.8921 | 6.96 | 800 | 2.6605 | 0.9982 |
| 1.3049 | 10.43 | 1200 | 0.5069 | 0.5707 |
| 1.1349 | 13.91 | 1600 | 0.4159 | 0.5041 |
| 1.0686 | 17.39 | 2000 | 0.3815 | 0.4746 |
| 0.999 | 20.87 | 2400 | 0.3541 | 0.4343 |
| 0.945 | 24.35 | 2800 | 0.3266 | 0.4132 |
| 0.9058 | 27.83 | 3200 | 0.2969 | 0.3771 |
| 0.8672 | 31.3 | 3600 | 0.2802 | 0.3553 |
| 0.8313 | 34.78 | 4000 | 0.2662 | 0.3380 |
| 0.8068 | 38.26 | 4400 | 0.2528 | 0.3181 |
| 0.7796 | 41.74 | 4800 | 0.2537 | 0.3073 |
| 0.7621 | 45.22 | 5200 | 0.2503 | 0.3036 |
| 0.7611 | 48.7 | 5600 | 0.2477 | 0.2991 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-bg --dataset mozilla-foundation/common_voice_8_0 --config bg --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-bg --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-large-xls-r-300m-bg"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "bg", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "и надутият му ката блоонкурем взе да се събира"
```
### Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 30.07 | 21.195 |
|
anuragshas/wav2vec2-xls-r-1b-hi-with-lm
|
anuragshas
| 2022-03-23T18:26:47Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
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
metrics:
- wer
model-index:
- name: XLS-R-1B - Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hi
metrics:
- name: Test WER
type: wer
value: 15.899
- name: Test CER
type: cer
value: 5.83
---
<!-- 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. -->
# XLS-R-1B - Hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6921
- Wer: 0.3547
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.0674 | 2.07 | 400 | 1.3411 | 0.8835 |
| 1.324 | 4.15 | 800 | 0.9311 | 0.7142 |
| 1.2023 | 6.22 | 1200 | 0.8060 | 0.6170 |
| 1.1573 | 8.29 | 1600 | 0.7415 | 0.4972 |
| 1.1117 | 10.36 | 2000 | 0.7248 | 0.4588 |
| 1.0672 | 12.44 | 2400 | 0.6729 | 0.4350 |
| 1.0336 | 14.51 | 2800 | 0.7117 | 0.4346 |
| 1.0025 | 16.58 | 3200 | 0.7019 | 0.4272 |
| 0.9578 | 18.65 | 3600 | 0.6792 | 0.4118 |
| 0.9272 | 20.73 | 4000 | 0.6863 | 0.4156 |
| 0.9321 | 22.8 | 4400 | 0.6535 | 0.3972 |
| 0.8802 | 24.87 | 4800 | 0.6766 | 0.3906 |
| 0.844 | 26.94 | 5200 | 0.6782 | 0.3949 |
| 0.8387 | 29.02 | 5600 | 0.6916 | 0.3921 |
| 0.8042 | 31.09 | 6000 | 0.6806 | 0.3797 |
| 0.793 | 33.16 | 6400 | 0.7120 | 0.3831 |
| 0.7567 | 35.23 | 6800 | 0.6862 | 0.3808 |
| 0.7463 | 37.31 | 7200 | 0.6893 | 0.3709 |
| 0.7053 | 39.38 | 7600 | 0.7096 | 0.3701 |
| 0.6906 | 41.45 | 8000 | 0.6921 | 0.3676 |
| 0.6891 | 43.52 | 8400 | 0.7167 | 0.3663 |
| 0.658 | 45.6 | 8800 | 0.6833 | 0.3580 |
| 0.6576 | 47.67 | 9200 | 0.6914 | 0.3569 |
| 0.6358 | 49.74 | 9600 | 0.6922 | 0.3551 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-1b-hi-with-lm --dataset mozilla-foundation/common_voice_8_0 --config hi --split test
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-xls-r-1b-hi-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "hi", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "तुम्हारे पास तीन महीने बचे हैं"
```
### Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 26.209 | 15.899 |
|
arampacha/wav2vec2-xls-r-1b-uk
|
arampacha
| 2022-03-23T18:26:29Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"uk",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- uk
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-1b-hy
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice uk
args: uk
metrics:
- type: wer
value: 10.406342913776015
name: WER LM
- type: cer
value: 2.0387492208601703
name: CER LM
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: uk
metrics:
- name: Test WER
type: wer
value: 40.57
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: uk
metrics:
- name: Test WER
type: wer
value: 28.95
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /WORKSPACE/DATA/UK/COMPOSED_DATASET/ - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1092
- Wer: 0.1752
- Cer: 0.0323
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 12000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 1.7005 | 1.61 | 500 | 0.4082 | 0.5584 | 0.1164 |
| 1.1555 | 3.22 | 1000 | 0.2020 | 0.2953 | 0.0557 |
| 1.0927 | 4.82 | 1500 | 0.1708 | 0.2584 | 0.0480 |
| 1.0707 | 6.43 | 2000 | 0.1563 | 0.2405 | 0.0450 |
| 1.0728 | 8.04 | 2500 | 0.1620 | 0.2442 | 0.0463 |
| 1.0268 | 9.65 | 3000 | 0.1588 | 0.2378 | 0.0458 |
| 1.0328 | 11.25 | 3500 | 0.1466 | 0.2352 | 0.0442 |
| 1.0249 | 12.86 | 4000 | 0.1552 | 0.2341 | 0.0449 |
| 1.016 | 14.47 | 4500 | 0.1602 | 0.2435 | 0.0473 |
| 1.0164 | 16.08 | 5000 | 0.1491 | 0.2337 | 0.0444 |
| 0.9935 | 17.68 | 5500 | 0.1539 | 0.2373 | 0.0458 |
| 0.9626 | 19.29 | 6000 | 0.1458 | 0.2305 | 0.0434 |
| 0.9505 | 20.9 | 6500 | 0.1368 | 0.2157 | 0.0407 |
| 0.9389 | 22.51 | 7000 | 0.1437 | 0.2231 | 0.0426 |
| 0.9129 | 24.12 | 7500 | 0.1313 | 0.2076 | 0.0394 |
| 0.9118 | 25.72 | 8000 | 0.1292 | 0.2040 | 0.0384 |
| 0.8848 | 27.33 | 8500 | 0.1299 | 0.2028 | 0.0384 |
| 0.8667 | 28.94 | 9000 | 0.1228 | 0.1945 | 0.0367 |
| 0.8641 | 30.55 | 9500 | 0.1223 | 0.1939 | 0.0364 |
| 0.8516 | 32.15 | 10000 | 0.1184 | 0.1876 | 0.0349 |
| 0.8379 | 33.76 | 10500 | 0.1137 | 0.1821 | 0.0338 |
| 0.8235 | 35.37 | 11000 | 0.1127 | 0.1779 | 0.0331 |
| 0.8112 | 36.98 | 11500 | 0.1103 | 0.1766 | 0.0327 |
| 0.8069 | 38.59 | 12000 | 0.1092 | 0.1752 | 0.0323 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
|
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