GetmanY1/wav2vec2-base-sami-22k

Sámi Wav2vec2-Base

The base model pre-trained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.

Note: This model does not have a tokenizer as it was pre-trained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for a more in-depth explanation of how to fine-tune the model.

Note: Fine-tuned version is available at GetmanY1/wav2vec2-base-sami-22k-finetuned

Model description

The Sámi Wav2Vec2 Base has the same architecture and uses the same training objective as the English one described in Paper. It is pre-trained on 22.4k hours of unlabeled Sámi speech from KAVI radio and television archive materials.

You can read more about the pre-trained model from this paper.

Intended uses & limitations

You can use this model for Sámi ASR (speech-to-text) and SER (Spoken Emotion Recognition) tasks.

How to use

See this notebook for more information on how to fine-tune the model.

Limitations and bias

This model was pre-trained with audio samples whose maximum length was 30 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.

The data used for pre-training was from the KAVI archives so this model might have biases towards the voices of radio hosts. The pre-training data was filtered via neural VAD, but some non-speech events like music might be still present in the training data, which might cause issues when fine-tuned on clear (no background noise) speech.

Training data

This model was pre-trained with 22.4k hours of Sámi speech data from the following sources:

Dataset	Hours	% of total hours
YleSámiRadio	22415 h	100 %

Datasets were filtered to include a maximum length of 30 seconds long audio samples.

Training procedure

Training was done on 256 AMD MI250x GPU modules (512 GPUs from the software perspective), using LUMI.

The training script was provided by Fairseq and it is available here.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 6e-04
• max_update: 125000
• seed: 1
• optimizer: 8-bit Adam with betas=(0.9,0.98) and epsilon=1e-06
• lr_scheduler_type: linear
• lr_scheduler_warmup_updates: 3000
• fp16: true
• max_sample_size: 960000
• min_sample_size: 32000
• normalize: false
• max_tokens: 2800000
• distributed_world_size: 512

The pre-trained model was initialized with the following hyperparameters:

• quantize_targets: true
• latent_temp: [2.0, 0.5, 0.999995]
• extractor_mode: default
• layer_norm_first: false
• dropout_input: 0.1
• dropout_features: 0.1
• feature_grad_mult: 0.1
• encoder_embed_dim: 768
• encoder_layers: 12
• encoder_ffn_embed_dim: 3072
• encoder_attention_heads: 12
• activation_fn: gelu
• dropout: 0.1
• attention_dropout: 0.1
• activation_dropout: 0.0
• encoder_layerdrop: 0.0

Training results

Training Loss	Epoch	Validation Loss
4.888	1	4.919
2.699	25	2.485
2.532	50	2.369
2.446	75	2.240
2.385	100	2.229
2.354	116	2.202

Framework versions

• Pytorch 2.3.0+rocm6.0
• Fairseq 0.12.2

Team Members

• Yaroslav Getman, Hugging Face profile, LinkedIn profile
• Tamas Grosz, Hugging Face profile, LinkedIn profile

Feel free to contact us for more details 🤗