---
license: apache-2.0
tags:
- automatic-speech-recognition
- fi
- finnish
library_name: transformers
language: fi
base_model:
- GetmanY1/wav2vec2-xlarge-fi-150k
model-index:
  - name: wav2vec2-xlarge-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: 14.98
          - name: Dev CER
            type: cer
            value: 4.13
          - name: Test WER
            type: wer
            value: 16.37
          - name: Test CER
            type: cer
            value: 5.03
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Finnish Parliament
          type: FinParl
          args: fi
        metrics:
          - name: Dev16 WER
            type: wer
            value: 10.91
          - name: Dev16 CER
            type: cer
            value: 4.85
          - name: Test16 WER
            type: wer
            value: 7.81
          - name: Test16 CER
            type: cer
            value: 3.48
          - name: Test20 WER
            type: wer
            value: 6.43
          - name: Test20 CER
            type: cer
            value: 2.09
      - 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: 6.65
        - name: Dev CER
          type: cer
          value: 1.15
        - name: Test WER
          type: wer
          value: 5.42
        - name: Test CER
          type: cer
          value: 0.96
      - 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: 8.67
        - name: Dev CER
          type: cer
          value: 5.18
        - name: Test WER
          type: wer
          value: 9.96
        - name: Test CER
          type: cer
          value: 5.74
---

# Finnish Wav2vec2-XLarge ASR

[GetmanY1/wav2vec2-xlarge-fi-150k](https://huggingface.co/GetmanY1/wav2vec2-xlarge-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 X-Large has the same architecture and uses the same training objective as the multilingual one described in [paper](https://www.isca-archive.org/interspeech_2022/babu22_interspeech.pdf).

[GetmanY1/wav2vec2-xlarge-fi-150k](https://huggingface.co/GetmanY1/wav2vec2-xlarge-fi-150k) is a large-scale, 1-billion 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](TODO). 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-xlarge-fi-150k-finetuned")
model = Wav2Vec2ForCTC.from_pretrained("GetmanY1/wav2vec2-xlarge-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)
```

## 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 🤗