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# m-ctc-t-german

This model is a fine-tuned version of [speechbrain/m-ctc-t-large](https://huggingface.co/speechbrain/m-ctc-t-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 14.0387
- Wer: 1.0000

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 448
- eval_batch_size: 448
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 896
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1883        | 1.0   | 511  | 3.5192          | 1.0    |
| 3.1097        | 2.0   | 1022 | 11.0713         | 1.0000 |
| 3.0541        | 3.0   | 1533 | 14.0387         | 1.0000 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "m-ctc-t-german", "results": []}]}
transformers pytorch tensorboard mctct automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #mctct #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

m-ctc-t-german
==============


This model is a fine-tuned version of speechbrain/m-ctc-t-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 14.0387
* Wer: 1.0000


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 448
* eval\_batch\_size: 448
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 896
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 448
* eval\_batch\_size: 448
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 896
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #mctct #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 448
* eval\_batch\_size: 448
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 896
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #mctct #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 448
* eval\_batch\_size: 448
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 896
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0
* Datasets 2.4.0
* Tokenizers 0.12.1