File size: 1,331 Bytes
d26912a c6f6a78 d26912a c6f6a78 c89034e 1e40ec9 c89034e 1e40ec9 c89034e 1e40ec9 45cee1b c89034e add8334 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
---
license: apache-2.0
datasets:
- KBLab/rixvox
language:
- sv
---
# 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 |