modelId
stringlengths 5
139
| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 18:27:06
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
ernestumorga/ppo-seals-Swimmer-v0
|
ernestumorga
| 2022-07-11T12:41:58Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Swimmer-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T12:40:57Z |
---
library_name: stable-baselines3
tags:
- seals/Swimmer-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 162.15 +/- 8.19
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Swimmer-v0
type: seals/Swimmer-v0
---
# **PPO** Agent playing **seals/Swimmer-v0**
This is a trained model of a **PPO** agent playing **seals/Swimmer-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo ppo --env seals/Swimmer-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/Swimmer-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Swimmer-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/Swimmer-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 8),
('clip_range', 0.1),
('ent_coef', 5.167107294612664e-08),
('gae_lambda', 0.95),
('gamma', 0.999),
('learning_rate', 0.0001214437022727675),
('max_grad_norm', 2),
('n_epochs', 20),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(activation_fn=nn.Tanh, net_arch=[dict(pi=[64, 64], vf=[64, '
'64])])'),
('vf_coef', 0.6162112311062333),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
ernestumorga/sac-seals-Humanoid-v0
|
ernestumorga
| 2022-07-11T12:40:34Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Humanoid-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T12:38:38Z |
---
library_name: stable-baselines3
tags:
- seals/Humanoid-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: -200.52 +/- 55.30
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Humanoid-v0
type: seals/Humanoid-v0
---
# **SAC** Agent playing **seals/Humanoid-v0**
This is a trained model of a **SAC** agent playing **seals/Humanoid-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo sac --env seals/Humanoid-v0 -orga ernestumorga -f logs/
python enjoy.py --algo sac --env seals/Humanoid-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo sac --env seals/Humanoid-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo sac --env seals/Humanoid-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('gamma', 0.98),
('learning_rate', 4.426351861707874e-05),
('learning_starts', 20000),
('n_timesteps', 2000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(net_arch=[400, 300], log_std_init=-0.1034412732183072)'),
('tau', 0.08),
('train_freq', 8),
('normalize', False)])
```
|
ernestumorga/ppo-seals-Humanoid-v0
|
ernestumorga
| 2022-07-11T12:36:37Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Humanoid-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-30T12:00:35Z |
---
library_name: stable-baselines3
tags:
- seals/Humanoid-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -43.69 +/- 155.83
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Humanoid-v0
type: seals/Humanoid-v0
---
# **PPO** Agent playing **seals/Humanoid-v0**
This is a trained model of a **PPO** agent playing **seals/Humanoid-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo ppo --env seals/Humanoid-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/Humanoid-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Humanoid-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/Humanoid-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 0.2),
('ent_coef', 2.0745206045994986e-05),
('gae_lambda', 0.92),
('gamma', 0.999),
('learning_rate', 2.0309225666232827e-05),
('max_grad_norm', 0.5),
('n_envs', 1),
('n_epochs', 20),
('n_steps', 2048),
('n_timesteps', 10000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[256, 256], '
'vf=[256, 256])])'),
('vf_coef', 0.819262464558427),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
jonatasgrosman/exp_w2v2t_es_vp-fr_s281
|
jonatasgrosman
| 2022-07-11T12:32:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T12:31:26Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-fr_s281
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ernestumorga/sac-seals-Ant-v0
|
ernestumorga
| 2022-07-11T12:29:54Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Ant-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T12:28:37Z |
---
library_name: stable-baselines3
tags:
- seals/Ant-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: 966.10 +/- 34.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Ant-v0
type: seals/Ant-v0
---
# **SAC** Agent playing **seals/Ant-v0**
This is a trained model of a **SAC** agent playing **seals/Ant-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo sac --env seals/Ant-v0 -orga ernestumorga -f logs/
python enjoy.py --algo sac --env seals/Ant-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo sac --env seals/Ant-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo sac --env seals/Ant-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('buffer_size', 1000000),
('gamma', 0.98),
('learning_rate', 0.0018514039303149058),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(net_arch=[256, 256], log_std_init=-2.2692589009754176)'),
('tau', 0.05),
('train_freq', 64),
('normalize', False)])
```
|
ernestumorga/sac-seals-HalfCheetah-v0
|
ernestumorga
| 2022-07-11T12:28:24Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/HalfCheetah-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T12:27:28Z |
---
library_name: stable-baselines3
tags:
- seals/HalfCheetah-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: 1474.73 +/- 33.37
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/HalfCheetah-v0
type: seals/HalfCheetah-v0
---
# **SAC** Agent playing **seals/HalfCheetah-v0**
This is a trained model of a **SAC** agent playing **seals/HalfCheetah-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo sac --env seals/HalfCheetah-v0 -orga ernestumorga -f logs/
python enjoy.py --algo sac --env seals/HalfCheetah-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo sac --env seals/HalfCheetah-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo sac --env seals/HalfCheetah-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 2048),
('buffer_size', 100000),
('gamma', 0.95),
('learning_rate', 0.000884624878315995),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(net_arch=[64, 64], log_std_init=-0.6932709443503001)'),
('tau', 0.01),
('train_freq', 64),
('normalize', False)])
```
|
ernestumorga/ppo-seals-Hopper-v0
|
ernestumorga
| 2022-07-11T12:27:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Hopper-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-30T11:59:04Z |
---
library_name: stable-baselines3
tags:
- seals/Hopper-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 2228.87 +/- 43.40
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Hopper-v0
type: seals/Hopper-v0
---
# **PPO** Agent playing **seals/Hopper-v0**
This is a trained model of a **PPO** agent playing **seals/Hopper-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo ppo --env seals/Hopper-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/Hopper-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Hopper-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/Hopper-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('clip_range', 0.1),
('ent_coef', 0.0010159833764878474),
('gae_lambda', 0.98),
('gamma', 0.995),
('learning_rate', 0.0003904770450788824),
('max_grad_norm', 0.9),
('n_envs', 1),
('n_epochs', 20),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[64, 64], vf=[64, '
'64])])'),
('vf_coef', 0.20315938606555833),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
ernestumorga/ppo-seals-Walker2d-v0
|
ernestumorga
| 2022-07-11T12:25:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Walker2d-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-30T10:53:25Z |
---
library_name: stable-baselines3
tags:
- seals/Walker2d-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 1429.13 +/- 411.75
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Walker2d-v0
type: seals/Walker2d-v0
---
# **PPO** Agent playing **seals/Walker2d-v0**
This is a trained model of a **PPO** agent playing **seals/Walker2d-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo ppo --env seals/Walker2d-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/Walker2d-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Walker2d-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/Walker2d-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 8),
('clip_range', 0.4),
('ent_coef', 0.00013057334805552262),
('gae_lambda', 0.92),
('gamma', 0.98),
('learning_rate', 3.791707778339674e-05),
('max_grad_norm', 0.6),
('n_envs', 1),
('n_epochs', 5),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[256, 256], '
'vf=[256, 256])])'),
('vf_coef', 0.6167177795726859),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
jonatasgrosman/exp_w2v2t_es_vp-fr_s169
|
jonatasgrosman
| 2022-07-11T12:18:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T12:17:50Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-fr_s169
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_unispeech-ml_s952
|
jonatasgrosman
| 2022-07-11T12:05:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T12:04:48Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech-ml_s952
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_wavlm_s655
|
jonatasgrosman
| 2022-07-11T11:44:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T11:43:35Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_wavlm_s655
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
rajkumarrrk/gpt-2-fine-tuned-on-cnn-dm
|
rajkumarrrk
| 2022-07-11T11:36:42Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-11T10:51:53Z |
---
license: apache-2.0
---
GPT-2 fine-tuned on CNN/DM summarization dataset.
Training args:\
{
"learning_rate": 0.0001\
"logging_steps": 5000\
"lr_scheduler_type": "cosine"\
"num_train_epochs": 2\
"per_device_train_batch_size": 12, # Total batch size: 36\
"weight_decay": 0.1\
}
{"generation_kwargs": {"do_sample": true, "max_new_tokens": 100, "min_length": 50}
Pre-processing to truncate the article to contain only 500 tokens.
Post-processing to consider only first three sentences as the summary.
Test split metrics:
Meteor: 0.2562237219960531\
Rouge1: 0.3754558158439447\
Rouge2: 0.15532626375157227\
RougeL: 0.25813023509572597\
RougeLsum: 0.3489472885043494\
BLEU: 0.09285941365815623\
Bert_score: 0.87570951795246\
|
jonatasgrosman/exp_w2v2t_es_vp-sv_s44
|
jonatasgrosman
| 2022-07-11T11:07:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T11:07:05Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-sv_s44
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_hubert_s251
|
jonatasgrosman
| 2022-07-11T10:59:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:59:03Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_hubert_s251
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_hubert_s456
|
jonatasgrosman
| 2022-07-11T10:56:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:55:43Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_hubert_s456
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_hubert_s459
|
jonatasgrosman
| 2022-07-11T10:52:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:52:20Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_hubert_s459
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_unispeech_s767
|
jonatasgrosman
| 2022-07-11T10:46:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:45:56Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech_s767
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_xlsr-53_s103
|
jonatasgrosman
| 2022-07-11T10:40:01Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:39:11Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_xlsr-53_s103
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_xlsr-53_s377
|
jonatasgrosman
| 2022-07-11T10:32:41Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:32:11Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_xlsr-53_s377
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AliMMZ/first_RL
|
AliMMZ
| 2022-07-11T10:26:59Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T09:56:49Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 265.62 +/- 14.05
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
jonatasgrosman/exp_w2v2t_es_wav2vec2_s875
|
jonatasgrosman
| 2022-07-11T10:19:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:18:46Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_wav2vec2_s875
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-it_s533
|
jonatasgrosman
| 2022-07-11T10:09:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:09:12Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-it_s533
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
fxmarty/20220711-h10m00s56_example_conll2003
|
fxmarty
| 2022-07-11T10:01:01Z | 0 | 0 | null |
[
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-07-11T10:00:56Z |
---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx512'}`
**Number of evaluation samples:** `100`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **node_exclusion**: `[]`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **quantization_approach**: `dynamic`, `static`
* **operators_to_quantize**: `['Add', 'MatMul']`, `['Add']`
# Evaluation
## Non-time metrics
| quantization_approach | operators_to_quantize | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :-------------------: | :-------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 0.974 | 0.974 | \| | 0.955 | 0.949 | \| | 0.964 | 0.962 | \| | 0.990 | 0.989 |
| `dynamic` | `['Add']` | \| | 0.974 | 0.974 | \| | 0.955 | 0.955 | \| | 0.964 | 0.964 | \| | 0.990 | 0.990 |
| `static` | `['Add', 'MatMul']` | \| | 0.974 | 0.081 | \| | 0.955 | 0.222 | \| | 0.964 | 0.118 | \| | 0.990 | 0.467 |
| `static` | `['Add']` | \| | 0.974 | 0.073 | \| | 0.955 | 0.182 | \| | 0.964 | 0.105 | \| | 0.990 | 0.290 |
## Time metrics
Time benchmarks were run for 3 seconds per config.
Below, time metrics for batch size = 1, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 59.35 | 21.91 | \| | 17.00 | 45.67 |
| `dynamic` | `['Add']` | \| | 59.18 | 29.24 | \| | 17.00 | 34.33 |
| `static` | `['Add', 'MatMul']` | \| | 59.25 | 28.31 | \| | 17.00 | 35.33 |
| `static` | `['Add']` | \| | 58.77 | 31.80 | \| | 17.33 | 31.67 |
|
jonatasgrosman/exp_w2v2t_ru_r-wav2vec2_s408
|
jonatasgrosman
| 2022-07-11T09:53:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:53:13Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_r-wav2vec2_s408
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_r-wav2vec2_s399
|
jonatasgrosman
| 2022-07-11T09:50:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:49:58Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_r-wav2vec2_s399
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
fxmarty/20220711-h09m49s39_example_conll2003
|
fxmarty
| 2022-07-11T09:49:44Z | 0 | 0 | null |
[
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-07-11T09:49:39Z |
---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx512'}`
**Number of evaluation samples:** `10`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **node_exclusion**: `[]`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **quantization_approach**: `dynamic`, `static`
* **operators_to_quantize**: `['Add', 'MatMul']`, `['Add']`
# Evaluation
## Non-time metrics
| quantization_approach | operators_to_quantize | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :-------------------: | :-------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 0.970 | 0.969 | \| | 0.970 | 0.939 | \| | 0.970 | 0.954 | \| | 0.993 | 0.990 |
| `dynamic` | `['Add']` | \| | 0.970 | 0.970 | \| | 0.970 | 0.970 | \| | 0.970 | 0.970 | \| | 0.993 | 0.993 |
| `static` | `['Add', 'MatMul']` | \| | 0.970 | 0.104 | \| | 0.970 | 0.212 | \| | 0.970 | 0.140 | \| | 0.993 | 0.691 |
| `static` | `['Add']` | \| | 0.970 | 0.037 | \| | 0.970 | 0.121 | \| | 0.970 | 0.057 | \| | 0.993 | 0.110 |
## Time metrics
Time benchmarks were run for 3 seconds per config.
Below, time metrics for batch size = 1, input length = 64.
| quantization_approach | operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :-------------------: | :-------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `dynamic` | `['Add', 'MatMul']` | \| | 60.12 | 18.13 | \| | 16.67 | 55.33 |
| `dynamic` | `['Add']` | \| | 59.49 | 29.12 | \| | 17.00 | 34.67 |
| `static` | `['Add', 'MatMul']` | \| | 58.89 | 24.30 | \| | 17.00 | 41.33 |
| `static` | `['Add']` | \| | 43.19 | 38.12 | \| | 23.33 | 26.33 |
|
jonatasgrosman/exp_w2v2t_ru_xls-r_s946
|
jonatasgrosman
| 2022-07-11T09:47:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:46:39Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_xls-r_s946
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_xls-r_s635
|
jonatasgrosman
| 2022-07-11T09:42:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:42:14Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_xls-r_s635
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_xls-r_s884
|
jonatasgrosman
| 2022-07-11T09:34:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:34:12Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_xls-r_s884
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
mrm8488/biomedtra-small-es-finetuned-bioasq-es
|
mrm8488
| 2022-07-11T09:27:29Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-11T08:09:31Z |
---
tags:
- generated_from_trainer
model-index:
- name: biomedtra-small-es-finetuned-bioasq-es
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. -->
# biomedtra-small-es-finetuned-bioasq-es
This model is a fine-tuned version of [mrm8488/biomedtra-small-es](https://huggingface.co/mrm8488/biomedtra-small-es) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9869
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.214 | 1.0 | 2802 | 2.1565 |
| 1.9885 | 2.0 | 5604 | 1.9794 |
| 1.9288 | 3.0 | 8406 | 1.9869 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_ru_vp-es_s664
|
jonatasgrosman
| 2022-07-11T09:07:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:07:23Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-es_s664
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-es_s729
|
jonatasgrosman
| 2022-07-11T09:04:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:04:06Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-es_s729
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_unispeech-ml_s569
|
jonatasgrosman
| 2022-07-11T08:48:36Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:48:11Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_unispeech-ml_s569
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_wavlm_s116
|
jonatasgrosman
| 2022-07-11T08:39:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:38:58Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_wavlm_s116
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_no-pretraining_s895
|
jonatasgrosman
| 2022-07-11T08:30:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:29:32Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_no-pretraining_s895
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-sv_s658
|
jonatasgrosman
| 2022-07-11T08:21:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:20:56Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-sv_s658
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_hubert_s732
|
jonatasgrosman
| 2022-07-11T08:10:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:10:28Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_hubert_s732
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_hubert_s818
|
jonatasgrosman
| 2022-07-11T08:07:47Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:07:21Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_hubert_s818
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
LeoFeng/superb_wav2vec_submit
|
LeoFeng
| 2022-07-11T08:05:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-07-11T07:57:49Z |
# SUPERB Submission Template
Welcome to the [SUPERB Challenge](https://superbbenchmark.org/challenge-slt2022/challenge_overview)! SUPERB is a collection of benchmarking resources to evaluate the capability of a universal shared representation for speech processing. It comes with a benchmark on the publicly available datasets and a challenge on a secret/not released hidden dataset. In SUPERB Challenge, a challenging hidden dataset is newly recorded to evaluate the ultimate generaliziblity across various tasks and data.
You can participate the challenge by simply submitting your self-supervised (SSL) pretrained models (model definition & pretrained weights), and we benchmark it with the hidden datasets. This repository constains useful tools to let you easliy [submit](https://superbbenchmark.org/submit) your models ***privately*** for evaluation to [the challenge hidden-set leaderboard](https://superbbenchmark.org/leaderboard?track=constrained&subset=Hidden+Dev+Set).
1. Generate a submission template
2. Validate the format/interface correctness of your model
3. Upload to Huggingface's Hub (privately)
4. Submit the upload information to [SUPERB website](https://superbbenchmark.org/submit)
#### Note 1.
We accept pre-trained models in PyTorch by default. If you wish to submit upstreams in non-PyTorch frameworks, please mail to [superb.announcement@gmail.com](mailto:superb.announcement@gmail.com)!
#### Note 2.
If you are not feasible to submit the pre-trained model, please mail to [superb.announcement@gmail.com](mailto:superb.announcement@gmail.com) for us to see how to help!
## Quickstart
### 1. Add model interfaces
#### forward
Extract features from waveforms.
- **Input:** A list of waveforms in 16000 Hz
```python
SAMPLE_RATE = 16000
BATCH_SIZE = 8
EXAMPLE_SEC = 10
wavs = [torch.randn(SAMPLE_RATE * EXAMPLE_SEC).cuda() for _ in range(BATCH_SIZE)]
```
- **Output:** A dictionary with a key "hidden_states" (for compatiblility with old ver.). The value is **a list** of padded sequences in the same shape of **(batch_size, max_sequence_length_of_batch, hidden_size)** for weighted-sum to work. It is welcome to perform some task-specified / independent pre- / post-processing on the upstream's raw hidden-sets, including upsampling and downsampling. However, all the values must come from **a single upstream model**:
```python
tasks = ["hidden_states", "PR", "SID", "ER", "ASR", "ASV", "SD", "QbE", "ST", "SS", "SE", "secret"]
for task in tasks:
# you can do task-specified pre- / post-processing depend on the arg "upstream_feature_selection"
results = upstream(wavs, upstream_feature_selection=task)
hidden_states = results["hidden_states"]
assert isinstance(results, dict)
assert isinstance(hidden_states, list)
for state in hidden_states:
assert isinstance(state, torch.Tensor)
assert state.dim() == 3, "(batch_size, max_sequence_length_of_batch, hidden_size)"
assert state.shape == hidden_states[0].shape
```
#### get_downsample_rates
Provide the downsample rate **from 16000 Hz waveforms** for each task's representation in the dict. For the standard 10ms stride representation, the downsample rate is 160.
```python
SAMPLE_RATE = 16000
MSEC_PER_SEC = 1000
downsample_rate = SAMPLE_RATE * 10 / MSEC_PER_SEC # 160
```
The downsample rate will be used to:
1. Calculate the valid representation length of each utterance in the output padded representation.
2. Prepare the training materials according to the representation's downsample rate for frame-level tasks, e.g. SD, SE, and SS.
- **Input:** the task key (str)
- **Output:** the downsample rate (int) of the representation for that task
```python
for task in tasks:
assert isinstance(task, str)
downsample_rate = upstream.get_downsample_rate(task)
assert isinstance(downsample_rate, int)
print("The upstream's representation for {task}"
f" has the downsample rate of {downsample_rate}.")
```
### 2. Create an account and organization on the Hugging Face Hub
First create an account on the Hugging Face Hub and you can sign up [here](https://huggingface.co/join) if you haven't already! Next, create a new organization and invite the SUPERB Hidden Set Committee to join. You will upload your model to a repository under this organization so that members inside it can access the model which is not publicly available.
* [superb-hidden-set](https://huggingface.co/superb-hidden-set)
### 3. Create a template repository on your machine
The next step is to create a template repository on your local machine that contains various files and a CLI to help you validate and submit your pretrained models. The Hugging Face Hub uses [Git Large File Storage (LFS)](https://git-lfs.github.com) to manage large files, so first install it if you don't have it already. For example, on macOS you can run:
```bash
brew install git-lfs
git lfs install
```
Next, run the following commands to create the repository. We recommend creating a Python virtual environment for the project, e.g. with Anaconda:
```bash
# Create and activate a virtual environment
conda create -n superb-submit python=3.8 && conda activate superb-submit
# Install the following libraries
pip install cookiecutter huggingface-hub==0.0.16
# Create the template repository
cookiecutter git+https://huggingface.co/superb/superb-submission
```
This will ask you to specify your Hugging Face Hub username, password, organisation, and the name of the repository:
```
hf_hub_username [<huggingface>]:
hf_hub_password [<password>]:
hf_hub_organisation [superb-submissions]:
repo_name [<my-superb-submissions>]:
```
This will trigger the following steps:
1. Create a private dataset repository on the Hugging Face Hub under `{hf_hub_organisation}/{repo_name}`
2. Clone the repository to your local machine
3. Add various template files, commit them locally to the repository, and push them to the Hub
The resulting repository should have the following structure:
```
my-superb-submission
├── LICENSE
├── README.md <- The README with submission instructions
├── cli.py <- The CLI for validating predictions etc
└── requirements.txt <- The requirements packages for the submissions
├── expert.py <- Your model definition
└── model.pt <- Your model weights
```
### 4. Install the dependencies
The final step is to install the project's dependencies:
```bash
# Navigate to the template repository
cd my-superb-submission
# Install dependencies
python -m pip install -r requirements.txt
```
That's it! You're now all set to start pretraining your speech models - see the instructions below on how to submit them to the Hub.
## Submitting to the leaderboard
To make a submission to the [leaderboard](https://superbbenchmark.org/leaderboard?subset=Hidden+Dev+Set), there are 4 main steps:
1. Modify `expert.py` and change `model.pt` so we can initialize an upstream model following the [challenge policy](https://superbbenchmark.org/challenge-slt2022/upstream) by:
```python
upstream = UpstreamExpert(ckpt="./model.pt")
```
***Package Dependency:*** Note that we only install `torch` package so far by following the above steps. If your model needs more packages, you can modify the `requirement.txt` to meet your need and install them inside the current conda environment. We will install the packages you list in the `requirement.txt` before initializing the upstream model.
2. Validate the upstream model's interface meets the requirements in the [challenge policy](https://superbbenchmark.org/challenge-slt2022/upstream). If everything is correct, you should see the following message: "All submission files validated! Now you can make a submission."
```
python cli.py validate
```
3. Push the model to the Hub! If there are no errors, you should see the following message: "Upload successful!"
```
python cli.py upload "commit message: my best model"
```
4. [Make a submission at SUPERB website](https://superbbenchmark.org/submit) by uniquely indentifying this uploaded model with the following information, which can be shown by:
```
python cli.py info
```
- Organization Name
- Repository Name
- Commit Hash (full 40 characters)
After you finish the above 4 steps. You will see a new entry in your [SUPERB profile page](https://superbbenchmark.org/profile) (need login) which does not have any benchmark numbers yet. Please wait for us to finetuned it on the hidden dataset and get the benchmark results. The results will be revealed within one week. Please stay tuned!
|
jonatasgrosman/exp_w2v2t_ru_hubert_s451
|
jonatasgrosman
| 2022-07-11T08:04:23Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:03:58Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_hubert_s451
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_xlsr-53_s911
|
jonatasgrosman
| 2022-07-11T07:52:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:51:37Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_xlsr-53_s911
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-100k_s334
|
jonatasgrosman
| 2022-07-11T07:42:16Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:41:33Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-100k_s334
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-100k_s732
|
jonatasgrosman
| 2022-07-11T07:39:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:38:17Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-100k_s732
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-it_s449
|
jonatasgrosman
| 2022-07-11T07:20:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:19:25Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-it_s449
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-it_s149
|
jonatasgrosman
| 2022-07-11T07:16:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:15:48Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-it_s149
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-it_s358
|
jonatasgrosman
| 2022-07-11T07:13:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:12:30Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-it_s358
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_r-wav2vec2_s925
|
jonatasgrosman
| 2022-07-11T07:09:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:09:27Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_r-wav2vec2_s925
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_r-wav2vec2_s668
|
jonatasgrosman
| 2022-07-11T07:05:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:05:12Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_r-wav2vec2_s668
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_xls-r_s79
|
jonatasgrosman
| 2022-07-11T06:57:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:57:05Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_xls-r_s79
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_xls-r_s831
|
jonatasgrosman
| 2022-07-11T06:54:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:53:49Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_xls-r_s831
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_unispeech-sat_s775
|
jonatasgrosman
| 2022-07-11T06:44:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:44:19Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_unispeech-sat_s775
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_unispeech-sat_s81
|
jonatasgrosman
| 2022-07-11T06:33:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:33:27Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_unispeech-sat_s81
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-nl_s747
|
jonatasgrosman
| 2022-07-11T06:29:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:28:49Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-nl_s747
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-nl_s158
|
jonatasgrosman
| 2022-07-11T06:26:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:25:51Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-nl_s158
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-nl_s424
|
jonatasgrosman
| 2022-07-11T06:23:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:22:52Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-nl_s424
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-es_s496
|
jonatasgrosman
| 2022-07-11T06:14:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:13:55Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-es_s496
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-fr_s226
|
jonatasgrosman
| 2022-07-11T06:11:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:10:50Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-fr_s226
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-fr_s417
|
jonatasgrosman
| 2022-07-11T06:08:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:07:50Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-fr_s417
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-fr_s156
|
jonatasgrosman
| 2022-07-11T06:05:18Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:04:52Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-fr_s156
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_unispeech-ml_s911
|
jonatasgrosman
| 2022-07-11T06:02:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T06:01:52Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_unispeech-ml_s911
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_unispeech-ml_s498
|
jonatasgrosman
| 2022-07-11T05:58:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T05:57:58Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_unispeech-ml_s498
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_unispeech-ml_s23
|
jonatasgrosman
| 2022-07-11T05:55:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T05:55:01Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_unispeech-ml_s23
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AndyChiang/bert-test
|
AndyChiang
| 2022-07-11T05:50:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-11T03:34:14Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: bert-test
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. -->
# bert-test
This model was trained from scratch 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 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_nl_wavlm_s213
|
jonatasgrosman
| 2022-07-11T05:49:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T05:48:51Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_wavlm_s213
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_no-pretraining_s512
|
jonatasgrosman
| 2022-07-11T05:43:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T05:42:54Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_no-pretraining_s512
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-sv_s510
|
jonatasgrosman
| 2022-07-11T05:34:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T05:33:38Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-sv_s510
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-sv_s607
|
jonatasgrosman
| 2022-07-11T05:27:33Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T05:27:07Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-sv_s607
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-sv_s703
|
jonatasgrosman
| 2022-07-11T05:21:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T05:20:54Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-sv_s703
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_hubert_s562
|
jonatasgrosman
| 2022-07-11T05:00:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T05:00:15Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_hubert_s562
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_hubert_s585
|
jonatasgrosman
| 2022-07-11T04:50:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T04:49:42Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_hubert_s585
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_hubert_s319
|
jonatasgrosman
| 2022-07-11T04:36:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T04:35:38Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_hubert_s319
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_unispeech_s493
|
jonatasgrosman
| 2022-07-11T04:15:55Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T04:15:16Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_unispeech_s493
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_unispeech_s853
|
jonatasgrosman
| 2022-07-11T04:05:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T04:05:02Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_unispeech_s853
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_xlsr-53_s948
|
jonatasgrosman
| 2022-07-11T03:52:19Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T03:51:53Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_xlsr-53_s948
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_xlsr-53_s972
|
jonatasgrosman
| 2022-07-11T03:31:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T03:31:17Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_xlsr-53_s972
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_xlsr-53_s799
|
jonatasgrosman
| 2022-07-11T03:28:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T03:27:38Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_xlsr-53_s799
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-100k_s408
|
jonatasgrosman
| 2022-07-11T03:23:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T03:22:58Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-100k_s408
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-100k_s772
|
jonatasgrosman
| 2022-07-11T03:19:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T03:18:34Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-100k_s772
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_vp-100k_s899
|
jonatasgrosman
| 2022-07-11T03:12:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T03:11:52Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_vp-100k_s899
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_nl_wav2vec2_s754
|
jonatasgrosman
| 2022-07-11T02:56:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T02:56:05Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- nl
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_nl_wav2vec2_s754
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_r-wav2vec2_s732
|
jonatasgrosman
| 2022-07-11T02:11:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T02:10:50Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_r-wav2vec2_s732
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_r-wav2vec2_s957
|
jonatasgrosman
| 2022-07-11T02:01:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T02:00:33Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_r-wav2vec2_s957
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_r-wav2vec2_s468
|
jonatasgrosman
| 2022-07-11T01:49:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T01:48:59Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_r-wav2vec2_s468
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_xls-r_s107
|
jonatasgrosman
| 2022-07-11T01:33:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T01:32:51Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_xls-r_s107
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_unispeech-sat_s108
|
jonatasgrosman
| 2022-07-11T01:09:54Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T01:09:13Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech-sat_s108
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_vp-nl_s797
|
jonatasgrosman
| 2022-07-11T00:49:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T00:48:59Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-nl_s797
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_vp-nl_s354
|
jonatasgrosman
| 2022-07-11T00:31:29Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T00:31:05Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-nl_s354
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_vp-nl_s353
|
jonatasgrosman
| 2022-07-11T00:25:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T00:25:27Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-nl_s353
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_vp-es_s3
|
jonatasgrosman
| 2022-07-11T00:07:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T00:07:13Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-es_s3
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_unispeech-ml_s545
|
jonatasgrosman
| 2022-07-10T23:44:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T23:44:05Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech-ml_s545
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_unispeech-ml_s527
|
jonatasgrosman
| 2022-07-10T23:36:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T23:35:36Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech-ml_s527
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_wavlm_s887
|
jonatasgrosman
| 2022-07-10T23:17:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T23:17:06Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_wavlm_s887
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_wavlm_s753
|
jonatasgrosman
| 2022-07-10T23:09:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T23:08:50Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_wavlm_s753
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_no-pretraining_s663
|
jonatasgrosman
| 2022-07-10T23:04:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T23:04:22Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_no-pretraining_s663
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_vp-sv_s953
|
jonatasgrosman
| 2022-07-10T22:47:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T22:46:56Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_vp-sv_s953
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_hubert_s507
|
jonatasgrosman
| 2022-07-10T22:41:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T22:40:39Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_hubert_s507
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_hubert_s118
|
jonatasgrosman
| 2022-07-10T22:38:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T22:37:32Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_hubert_s118
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
malinoori/wav2vec2-base-2
|
malinoori
| 2022-07-10T22:33:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T20:17:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-2
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-base-2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5953
- eval_wer: 0.3621
- eval_runtime: 54.4895
- eval_samples_per_second: 30.832
- eval_steps_per_second: 3.854
- epoch: 22.61
- step: 22500
## 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: 4
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_et_unispeech_s177
|
jonatasgrosman
| 2022-07-10T22:31:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T22:31:32Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech_s177
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_et_unispeech_s605
|
jonatasgrosman
| 2022-07-10T22:28:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"et",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-10T22:28:31Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- et
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_et_unispeech_s605
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
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The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.