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
Runs of KBLab whisper-tiny-rixvox on huggingface.co
5
Total runs
0
24-hour runs
0
3-day runs
1
7-day runs
0
30-day runs
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