metadata

license: apache-2.0
language: fi
metrics:
  - wer
  - cer
tags:
  - automatic-speech-recognition
  - fi
  - finnish
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: wav2vec2-xlsr-1b-finnish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: fi
        metrics:
          - name: Test WER
            type: wer
            value: 13.11
          - name: Test CER
            type: cer
            value: 2.23

Wav2Vec2 XLS-R for Finnish ASR

This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-1b for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in this paper and first released at this page.

Note: there is a version with KenLM language model used in the decoding phase producing better transcriptions: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm

Note: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2

Model description

Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.

You can read more about the pretrained model from this blog and this paper.

This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.

Intended uses & limitations

You can use this model for Finnish ASR (speech-to-text) task.

How to use

Check the run-finnish-asr-models.ipynb notebook in this repository for an detailed example on how to use this model.

Limitations and bias

This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.

A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.

Training data

This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:

Dataset	Hours	% of total hours
Common Voice 7.0 Finnish train + evaluation + other splits	9.70 h	3.74 %
Finnish parliament session 2	0.24 h	0.09 %
VoxPopuli Finnish	5.94 h	2.29 %
CSS10 Finnish	10.32 h	3.98 %
Aalto Finnish Parliament ASR Corpus	228.00 h	87.84 %
Finnish Broadcast Corpus	5.37 h	2.07 %

Datasets were filtered to include maximum length of 20 seconds long audio samples.

Training procedure

This model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.

Training script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: 8-bit Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5
mixed_precision_training: Native AMP

The pretrained facebook/wav2vec2-xls-r-1b model was initialized with following hyperparameters:

attention_dropout: 0.094
hidden_dropout: 0.047
feat_proj_dropout: 0.04
mask_time_prob: 0.082
layerdrop: 0.041
activation_dropout: 0.055
ctc_loss_reduction: "mean"

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.968	0.18	500	0.4870	0.4720
0.6557	0.36	1000	0.2450	0.2931
0.647	0.54	1500	0.1818	0.2255
0.5297	0.72	2000	0.1698	0.2354
0.5802	0.9	2500	0.1581	0.2355
0.6351	1.07	3000	0.1689	0.2336
0.4626	1.25	3500	0.1719	0.3099
0.4526	1.43	4000	0.1434	0.2069
0.4692	1.61	4500	0.1645	0.2192
0.4584	1.79	5000	0.1483	0.1987
0.4234	1.97	5500	0.1499	0.2178
0.4243	2.15	6000	0.1345	0.2070
0.4108	2.33	6500	0.1383	0.1850
0.4048	2.51	7000	0.1338	0.1811
0.4085	2.69	7500	0.1290	0.1780
0.4026	2.87	8000	0.1239	0.1650
0.4033	3.04	8500	0.1346	0.1657
0.3986	3.22	9000	0.1310	0.1850
0.3867	3.4	9500	0.1273	0.1741
0.3658	3.58	10000	0.1219	0.1672
0.382	3.76	10500	0.1306	0.1698
0.3847	3.94	11000	0.1230	0.1577
0.3691	4.12	11500	0.1310	0.1615
0.3593	4.3	12000	0.1296	0.1622
0.3619	4.48	12500	0.1285	0.1601
0.3361	4.66	13000	0.1261	0.1569
0.3603	4.84	13500	0.1235	0.1533

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.3
Tokenizers 0.11.0

Evaluation results

Evaluation was done with the Common Voice 7.0 Finnish test split.

To evaluate this model, run the eval.py script in this repository:

python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test

This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:

	WER (with LM)	WER (without LM)	CER (with LM)	CER (without LM)
aapot/wav2vec2-xlsr-1b-finnish-lm-v2	4.09	9.73	0.88	1.65
aapot/wav2vec2-xlsr-1b-finnish-lm	5.65	13.11	1.20	2.23
aapot/wav2vec2-xlsr-300m-finnish-lm	8.16	17.92	1.97	3.36

Team Members

Aapo Tanskanen, Hugging Face profile, LinkedIn profile
Rasmus Toivanen, Hugging Face profile, LinkedIn profile

Feel free to contact us for more details 🤗