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Model: GetmanY1/wav2vec2-large-fi-150k-finetuned
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---
license: apache-2.0
tags:
- automatic-speech-recognition
- fi
- finnish
library_name: transformers
language: fi
base_model:
- GetmanY1/wav2vec2-large-fi-150k
model-index:
- name: wav2vec2-large-fi-150k-finetuned
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Lahjoita puhetta (Donate Speech)
type: lahjoita-puhetta
args: fi
metrics:
- name: Dev WER
type: wer
value: 15.34
- name: Dev CER
type: cer
value: 4.14
- name: Test WER
type: wer
value: 16.86
- name: Test CER
type: cer
value: 5.07
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Finnish Parliament
type: FinParl
args: fi
metrics:
- name: Dev16 WER
type: wer
value: 11.3
- name: Dev16 CER
type: cer
value: 4.75
- name: Test16 WER
type: wer
value: 8.29
- name: Test16 CER
type: cer
value: 3.34
- name: Test20 WER
type: wer
value: 6.94
- name: Test20 CER
type: cer
value: 2.15
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
args: fi
metrics:
- name: Dev WER
type: wer
value: 7.17
- name: Dev CER
type: cer
value: 1.11
- name: Test WER
type: wer
value: 5.86
- name: Test CER
type: cer
value: 0.91
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: FLEURS
type: google/fleurs
args: fi_fi
metrics:
- name: Dev WER
type: wer
value: 9.2
- name: Dev CER
type: cer
value: 5.23
- name: Test WER
type: wer
value: 10.69
- name: Test CER
type: cer
value: 5.79
---
# Finnish Wav2vec2-Large ASR
[GetmanY1/wav2vec2-large-fi-150k](https://huggingface.co/GetmanY1/wav2vec2-large-fi-150k) fine-tuned on 4600 hours of Finnish speech on 16kHz sampled speech audio:
* 1500 hours of [Lahjoita puhetta (Donate Speech)](https://link.springer.com/article/10.1007/s10579-022-09606-3) (colloquial Finnish)
* 3100 hours of the [Finnish Parliament dataset](https://link.springer.com/article/10.1007/s10579-023-09650-7)
When using the model make sure that your speech input is also sampled at 16Khz.
## Model description
The Finnish Wav2Vec2 Large has the same architecture and uses the same training objective as the English and multilingual one described in [Paper](https://arxiv.org/abs/2006.11477).
[GetmanY1/wav2vec2-large-fi-150k](https://huggingface.co/GetmanY1/wav2vec2-large-fi-150k) is a large-scale, 317-million parameter monolingual model pre-trained on 158k hours of unlabeled Finnish speech, including [KAVI radio and television archive materials](https://kavi.fi/en/radio-ja-televisioarkistointia-vuodesta-2008/), Lahjoita puhetta (Donate Speech), Finnish Parliament, Finnish VoxPopuli.
You can read more about the pre-trained model from [this paper](https://www.isca-archive.org/interspeech_2025/getman25_interspeech.html). The training scripts are available on [GitHub](https://github.com/aalto-speech/large-scale-monolingual-speech-foundation-models).
## Intended uses
You can use this model for Finnish ASR (speech-to-text).
### How to use
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("GetmanY1/wav2vec2-large-fi-150k-finetuned")
model = Wav2Vec2ForCTC.from_pretrained("GetmanY1/wav2vec2-large-fi-150k-finetuned")
# load dummy dataset and read soundfiles
ds = load_dataset("mozilla-foundation/common_voice_16_1", "fi", split='test')
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Citation
If you use our models or scripts, please cite our article as:
```bibtex
@inproceedings{getman25_interspeech,
title = {{Is your model big enough? Training and interpreting large-scale monolingual speech foundation models}},
author = {{Yaroslav Getman and Tamás Grósz and Tommi Lehtonen and Mikko Kurimo}},
year = {{2025}},
booktitle = {{Interspeech 2025}},
pages = {{231--235}},
doi = {{10.21437/Interspeech.2025-46}},
issn = {{2958-1796}},
}
```
## Team Members
- Yaroslav Getman, [Hugging Face profile](https://huggingface.co/GetmanY1), [LinkedIn profile](https://www.linkedin.com/in/yaroslav-getman/)
- Tamas Grosz, [Hugging Face profile](https://huggingface.co/Grosy), [LinkedIn profile](https://www.linkedin.com/in/tam%C3%A1s-gr%C3%B3sz-950a049a/)
Feel free to contact us for more details 🤗

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"xvector_output_dim": 512
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