# Whisper Tiny RixVox Swedish

This is a [Whisper tiny](https://huggingface.co/openai/whisper-tiny) finetuned for Swedish using
the [RixVox](https://huggingface.co/datasets/KBLab/rixvox) dataset.

Please note that this model, as every other encoder-decoder speech-to-text model, is prone to
hallucinating on unexpected inputs and treats the task as translation rather than transcription.
I.e your mileage may vary depending on filtering and type of data.

In this release the entire encoder was frozen. Subsequent releases will not do this **if** the
generalization to other types of data (i.e not parliamentary speeches) is kept when not freezing
the encoder.

## Evaluation

* Fleurs WER: 51.68
* Fleurs WER (normalized*): 48.09

*) Normalization is done by applying the following to source and generated texts:

```
def normalize(s):
    return ' '.join([ x for x in sub('[^0-9a-zåäöA-ZÅÄÖ ]', ' ', s.lower()).split() ])
```

## Training

Training was done using Huggingface and Deepspeed with ZeRO stage 2.

* learning rate: 1e-5
* optimizer: CPUAdamW (Deepspeed)
* lr scheduler: linear
* warmup steps: 500
* per device batch size: 32
* GPUs: 8 x NVIDIA A100 40GB
* total batch size: 160
* steps: 10000
* lowercase: no
* fp16
* entire encoder was frozen
{"language": ["sv"], "license": "apache-2.0", "datasets": ["KBLab/rixvox"]}
transformers pytorch tensorboard whisper automatic-speech-recognition sv dataset:KBLab/rixvox license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #sv #dataset-KBLab/rixvox #license-apache-2.0 #endpoints_compatible #region-us 

# Whisper Tiny RixVox Swedish

This is a Whisper tiny finetuned for Swedish using
the RixVox dataset.

Please note that this model, as every other encoder-decoder speech-to-text model, is prone to
hallucinating on unexpected inputs and treats the task as translation rather than transcription.
I.e your mileage may vary depending on filtering and type of data.

In this release the entire encoder was frozen. Subsequent releases will not do this if the
generalization to other types of data (i.e not parliamentary speeches) is kept when not freezing
the encoder.

## Evaluation

* Fleurs WER: 51.68
* Fleurs WER (normalized*): 48.09

*) Normalization is done by applying the following to source and generated texts:



## Training

Training was done using Huggingface and Deepspeed with ZeRO stage 2.

* learning rate: 1e-5
* optimizer: CPUAdamW (Deepspeed)
* lr scheduler: linear
* warmup steps: 500
* per device batch size: 32
* GPUs: 8 x NVIDIA A100 40GB
* total batch size: 160
* steps: 10000
* lowercase: no
* fp16
* entire encoder was frozen
# Whisper Tiny RixVox Swedish

This is a Whisper tiny finetuned for Swedish using
the RixVox dataset.

Please note that this model, as every other encoder-decoder speech-to-text model, is prone to
hallucinating on unexpected inputs and treats the task as translation rather than transcription.
I.e your mileage may vary depending on filtering and type of data.

In this release the entire encoder was frozen. Subsequent releases will not do this if the
generalization to other types of data (i.e not parliamentary speeches) is kept when not freezing
the encoder. ## Evaluation

* Fleurs WER: 51.68
* Fleurs WER (normalized*): 48.09

*) Normalization is done by applying the following to source and generated texts: ## Training

Training was done using Huggingface and Deepspeed with ZeRO stage 2.

* learning rate: 1e-5
* optimizer: CPUAdamW (Deepspeed)
* lr scheduler: linear
* warmup steps: 500
* per device batch size: 32
* GPUs: 8 x NVIDIA A100 40GB
* total batch size: 160
* steps: 10000
* lowercase: no
* fp16
* entire encoder was frozen
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #sv #dataset-KBLab/rixvox #license-apache-2.0 #endpoints_compatible #region-us 
 # Whisper Tiny RixVox Swedish

This is a Whisper tiny finetuned for Swedish using
the RixVox dataset.

Please note that this model, as every other encoder-decoder speech-to-text model, is prone to
hallucinating on unexpected inputs and treats the task as translation rather than transcription.
I.e your mileage may vary depending on filtering and type of data.

In this release the entire encoder was frozen. Subsequent releases will not do this if the
generalization to other types of data (i.e not parliamentary speeches) is kept when not freezing
the encoder. ## Evaluation

* Fleurs WER: 51.68
* Fleurs WER (normalized*): 48.09

*) Normalization is done by applying the following to source and generated texts: ## Training

Training was done using Huggingface and Deepspeed with ZeRO stage 2.

* learning rate: 1e-5
* optimizer: CPUAdamW (Deepspeed)
* lr scheduler: linear
* warmup steps: 500
* per device batch size: 32
* GPUs: 8 x NVIDIA A100 40GB
* total batch size: 160
* steps: 10000
* lowercase: no
* fp16
* entire encoder was frozen
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #sv #dataset-KBLab/rixvox #license-apache-2.0 #endpoints_compatible #region-us 
# Whisper Tiny RixVox Swedish

This is a Whisper tiny finetuned for Swedish using
the RixVox dataset.

Please note that this model, as every other encoder-decoder speech-to-text model, is prone to
hallucinating on unexpected inputs and treats the task as translation rather than transcription.
I.e your mileage may vary depending on filtering and type of data.

In this release the entire encoder was frozen. Subsequent releases will not do this if the
generalization to other types of data (i.e not parliamentary speeches) is kept when not freezing
the encoder.## Evaluation

* Fleurs WER: 51.68
* Fleurs WER (normalized*): 48.09

*) Normalization is done by applying the following to source and generated texts:## Training

Training was done using Huggingface and Deepspeed with ZeRO stage 2.

* learning rate: 1e-5
* optimizer: CPUAdamW (Deepspeed)
* lr scheduler: linear
* warmup steps: 500
* per device batch size: 32
* GPUs: 8 x NVIDIA A100 40GB
* total batch size: 160
* steps: 10000
* lowercase: no
* fp16
* entire encoder was frozen