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# tiny_scream_april_beta

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the NbAiLab/NCC_speech_all_v5 dataset. It uses a beam size of 5.

## Model description

This is a BETA version. You need to accept [the terms and conditons](license.md) to use it.

## Using the Model
There are several ways of using this model, and we do hope people will convert it into different formats. The code below allows you to process long files with Transformers.:

```python
import torch
import numpy as np
import librosa
from transformers import pipeline

# Try using "mps" for Metal (Mac), "cuda" if you have GPU, and "cpu" if not
device = torch.device("cuda")

pipe = pipeline("automatic-speech-recognition",
      model="NbAiLab/tiny_scream_april_beta",
      chunk_length_s=30,
      device=device,
      max_new_tokens=128,
      generate_kwargs={"language": "", "task": "transcribe"})

# Load the WAV file. Modify this to use mp3 instead
audio_path = 'myfile.wav'
samples, sample_rate = librosa.load(audio_path, sr=16000, mono=True)

# Run the pipeline
prediction = pipe(samples)["text"]

print(prediction)

```


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8e-05
- lr_scheduler_type: linear
- per_device_train_batch_size: 48
- total_train_batch_size_per_node: 192
- total_train_batch_size: 1536
- total_optimization_steps: 50000
- starting_optimization_step: None
- finishing_optimization_step: 50000
- num_train_dataset_workers: 64
- total_num_training_examples: 76800000

### Training results

| step  | eval_loss | train_loss | eval_wer | eval_cer |
|:-----:|:---------:|:----------:|:--------:|:--------:|
| 0     | 2.1853    | 2.6128     | 225.2741 | 151.0305 |
| 2500  | 0.8090    | 0.6776     | 26.0049  | 10.4006  |
| 5000  | 0.5674    | 0.5277     | 20.7674  | 8.7327   |
| 7500  | 0.5255    | 0.4551     | 19.3971  | 8.5059   |
| 10000 | 0.5774    | 0.4327     | 18.0877  | 8.0272   |


### Framework versions

- Transformers 4.28.0.dev0
- Datasets 2.11.0
- Tokenizers 0.13.2

{"language": ["no"], "license": "apache-2.0", "tags": ["audio", "asr", "automatic-speech-recognition", "hf-asr-leaderboard"], "extra_gated_prompt": "This is a BETA-model. To use this model, you agree on the [licensing terms](license.md).", "model-index": [{"name": "tiny_scream_april_beta", "results": []}]}
transformers pytorch jax tensorboard whisper automatic-speech-recognition audio asr hf-asr-leaderboard no license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #tensorboard #whisper #automatic-speech-recognition #audio #asr #hf-asr-leaderboard #no #license-apache-2.0 #endpoints_compatible #region-us 

tiny\_scream\_april\_beta
=========================


This model is a fine-tuned version of openai/whisper-tiny on the NbAiLab/NCC\_speech\_all\_v5 dataset. It uses a beam size of 5.


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


This is a BETA version. You need to accept the terms and conditons to use it.


Using the Model
---------------


There are several ways of using this model, and we do hope people will convert it into different formats. The code below allows you to process long files with Transformers.:


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 48
* total\_train\_batch\_size\_per\_node: 192
* total\_train\_batch\_size: 1536
* total\_optimization\_steps: 50000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 50000
* num\_train\_dataset\_workers: 64
* total\_num\_training\_examples: 76800000


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 48
* total\_train\_batch\_size\_per\_node: 192
* total\_train\_batch\_size: 1536
* total\_optimization\_steps: 50000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 50000
* num\_train\_dataset\_workers: 64
* total\_num\_training\_examples: 76800000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #jax #tensorboard #whisper #automatic-speech-recognition #audio #asr #hf-asr-leaderboard #no #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 48
* total\_train\_batch\_size\_per\_node: 192
* total\_train\_batch\_size: 1536
* total\_optimization\_steps: 50000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 50000
* num\_train\_dataset\_workers: 64
* total\_num\_training\_examples: 76800000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #jax #tensorboard #whisper #automatic-speech-recognition #audio #asr #hf-asr-leaderboard #no #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 48
* total\_train\_batch\_size\_per\_node: 192
* total\_train\_batch\_size: 1536
* total\_optimization\_steps: 50000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 50000
* num\_train\_dataset\_workers: 64
* total\_num\_training\_examples: 76800000### Training results### Framework versions


* Transformers 4.28.0.dev0
* Datasets 2.11.0
* Tokenizers 0.13.2