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# Whisper Medium FLEURS Language Identification

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the [FLEURS subset](https://huggingface.co/datasets/google/xtreme_s#language-identification---fleurs-langid) of the [google/xtreme_s](https://huggingface.co/google/xtreme_s) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8413
- Accuracy: 0.8805

To reproduce this run, execute the command in [`run.sh`](https://huggingface.co/sanchit-gandhi/whisper-medium-fleurs-lang-id/blob/main/run.sh).

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 0
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0152        | 1.0   | 8494  | 0.9087          | 0.8431   |
| 0.0003        | 2.0   | 16988 | 1.0059          | 0.8460   |
| 0.0           | 3.0   | 25482 | 0.8413          | 0.8805   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["accuracy"], "base_model": "openai/whisper-medium", "model-index": [{"name": "whisper-medium-fleurs-lang-id", "results": []}]}
transformers pytorch tensorboard safetensors whisper audio-classification generated_from_trainer dataset:xtreme_s base_model:openai/whisper-medium license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #audio-classification #generated_from_trainer #dataset-xtreme_s #base_model-openai/whisper-medium #license-apache-2.0 #endpoints_compatible #has_space #region-us 

Whisper Medium FLEURS Language Identification
=============================================


This model is a fine-tuned version of openai/whisper-medium on the FLEURS subset of the google/xtreme\_s dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8413
* Accuracy: 0.8805


To reproduce this run, execute the command in 'URL'.


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


More information needed


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


More information needed


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


More information needed


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 0
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 0
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #audio-classification #generated_from_trainer #dataset-xtreme_s #base_model-openai/whisper-medium #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 0
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #audio-classification #generated_from_trainer #dataset-xtreme_s #base_model-openai/whisper-medium #license-apache-2.0 #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 0
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3.0### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2