BSC-LT/hubert-base-los-2k

Table of Contents

Click to expand

• Model Description
• Intended Uses and Limitations
• Pre-training Details
• Indirect evaluation results
• How to use the model
• Citation
• Additional Information

Model Description

This is a HuBERT Base model pre-trained using 2,000 hours of Iberian languages speech data (Spanish, Catalan, Basque, and Galician). The model architecture is the same as the original HuBERT Base model, which contains 12 transformer layers. Pre-training was done by Barcelona Supercomputing Center.

Intended Uses and Limitations

This pre-trained model generates Speech Representations that can be used for any Iberian speech-related task. This model does not have a tokenizer as it was pretrained on audio alone.

In order to use this model for Automatic Speech Recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model for Speech Recognition. For an explanation of how to fine-tune the model for Audio Classification, check out this tutorial.

Pre-training Details

This model was pre-trained using code from the official repository, and the detailed training configuration can be found in the same repository and the original paper.

For pre-training, a 2,000 hours dataset was created using subsets from training splits from the following datasets:

Dataset	Language	Selected hours	Comments
Basque Parliament Speech Corpus 1.0	Spanish	191
VoxPopuli	Spanish	152
CommonVoice 21	Spanish	120
VoxForge Spanish	Spanish	37
Catalan Youtube Speech	Catalan	170
3CatParla	Catalan	170	This dataset is private and is planned to be published as public soon.
CommonVoice 21	Catalan	44
Corts Valencianes	Catalan	44	Only the anonymized version of the dataset is public. We trained the model with the non-anonymized version.
parlament_parla_v3	Catalan	44	This dataset is private and is planned to be published as public soon.
IB3 - Speech Corpus for Catalan-varieties ASR	Catalan	28	This dataset is private and is planned to be published as public soon.
Basque Parliament Speech Corpus 1.0	Basque	334
CommonVoice 21	Basque	166
Nos_RG-Podcast-GL	Galician	250
Nos_ParlaSpeech-GL	Galician	100
CommonVoice 21	Galician	90
Nos_Transcrispeech-GL	Galician	35
Nos_Celtia-GL	Galician	25

Indirect evaluation results

To assess the pre-trained Catalan Speech Representations' quality, we evaluated them using two indirect tasks: Automatic Speech Recognition (ASR) and Language Identification (LID).

Automatic Speech Recognition

We created train and validation ASR-labelled datasets using a 400 hours subsample from the pre-training dataset split. For testing, we created a test split concatenating all the test splits from:

• CommonVoice 21
• Basque Parliament Speech Corpus 1.0
• VoxPopuli
• 3CatParla
• Corts Valencianes
• parlament_parla_v3
• Catalan Youtube Speech
• Nos_ParlaSpeech-GL
• Nos_RG-Podcast-GL
• Nos_Transcrispeech-GL

We fine-tuned on this ASR-labelled 400 hours training split the following models:

• Iberian pre-trained HuBERT: BSC-LT/hubert-base-los-2k (our model)
• English pre-trained HuBERT: facebook/hubert-base-ls960
• Multi-lingual pre-trained HuBERT: utter-project/mHuBERT-147

All of these models were pre-trained using exactly the same configurations. We trained them for 20 epochs. For the fine-tuning process, we froze models' parameters using the freeze_feature_encoder() method. hubert-base-los-2k, hubert-base-ls960 and mHuBERT-147 have 94M parameters, 95% of them were fine-tuned. The results were the following:

Model	Train WER	Validation WER	Test WER ↑
hubert-base-los-2k	5.6%	8.5%	12.8%
mHuBERT-147	8.1%	11.2%	15.9%
hubert-base-ls960	11.6%	15.2%	20.7%

Language Identification

We created train and validation Language Identification labelled datasets using a 200 hours subsample from the pre-training dataset split (excluding Common Voice splits). For testing, we created a test split concatenating all the Spanish, Catalan, Basque, and Galician test splits from CommonVoice 21.

We fine-tuned on this 200 hours labelled training split the following models:

• Iberian pre-trained HuBERT: BSC-LT/hubert-base-los-2k (our model)
• English pre-trained HuBERT: facebook/hubert-base-ls960
• Multi-lingual pre-trained HuBERT: utter-project/mHuBERT-147

All of these models were pre-trained using exactly the same configurations. We trained them for 10 epochs. For the fine-tuning process, we froze models' parameters using the freeze_base_model() method. hubert-base-los-2k, hubert-base-ls960 and mHuBERT-147 have 94M parameters, 0.2% of them were fine-tuned.

The results were the following:

Model	Train f1-macro	Validation f1-macro	Test f1-macro ↓
hubert-base-los-2k	99.6%	99.6%	69.2%
mHuBERT-147	97.2%	97.6%	37.6%
hubert-base-ls960	91.6%	92.1%	20.3%

How to use the model

Speech Representations

To obtain Speech Representations (HuBERT outputs) from audio in Iberian languages using this model, you can follow this example:

(Using fsspec==2025.3.0, datasets==3.6.0 and transformers==4.52.2 is recomended).

from datasets import load_dataset, Audio
import torch
from transformers import AutoFeatureExtractor, AutoModel

#Load the dataset
dataset = load_dataset("projecte-aina/ib3_ca_asr", split='train[:1%]', trust_remote_code=True)

#Downsample to 16kHz
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))

# Hugginface pre-trained model path
MODEL_NAME = "BSC-LT/hubert-base-los-2k"

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using {device} device.")

# Load feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_NAME)

# Load model
model = AutoModel.from_pretrained(MODEL_NAME)
model = model.to(device)

def map_to_speech_representations(batch):

  #Process the dataset
  audio = batch["audio"]
  input_features = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_values
  input_features = input_features.to(device)

  # Extract HuBERT's Speech Representations
  with torch.no_grad():
    outputs = model(
        input_features,
        output_hidden_states = True,
        )
    speech_representations = outputs.last_hidden_state
    hidden_states = outputs.hidden_states

  batch["speech_representations"] = speech_representations
  batch["hidden_states"] = hidden_states

  return batch

dataset = dataset.map(map_to_speech_representations)

print(dataset)

Discrete Speech Representations

Important remark: the k-means model available in this repo and used for extracting Discrete Speech Representations was trained using HuBERT's 6th layer.

To obtain Discrete Speech Representations (HuBERT's k-means centroids) from audio in Iberian languages using this model, you can follow this example:

(Using fsspec==2025.3.0, datasets==3.6.0 and transformers==4.52.2 is recomended).

from datasets import load_dataset, Audio
import torch
from transformers import AutoFeatureExtractor, AutoModel
import joblib
import numpy as np
from huggingface_hub import hf_hub_download

#Load the dataset
dataset = load_dataset("projecte-aina/ib3_ca_asr", split='train[:1%]', trust_remote_code=True)

#Downsample to 16kHz
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))

# Hugginface pre-trained model path
MODEL_NAME = "BSC-LT/hubert-base-los-2k"

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using {device} device.")

# Load feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_NAME)

# Load model
model = AutoModel.from_pretrained(MODEL_NAME)
model = model.to(device)

# Load k-means
km_path = hf_hub_download(repo_id="BSC-LT/hubert-base-los-2k", filename="k_means.km")
km_model = joblib.load(km_path)
clusters = km_model.cluster_centers_

def map_to_discrete_units(batch):

  #Process the dataset
  audio = batch["audio"]
  input_features = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_values
  input_features = input_features.to(device)

  
  with torch.no_grad():
    outputs = model(
        input_features,
        output_hidden_states = True,
        )
    
    # Extract HuBERT's Speech Representations
    hidden_states = outputs.hidden_states

    # Extract 6-th layer features
    k_means_input = hidden_states[5].squeeze()
    k_means_input = k_means_input.cpu()
    k_means_input = np.array(k_means_input, dtype='f')
    
    labels = km_model.predict(k_means_input)
    batch["discrete_units"] = clusters[labels]

  return batch

dataset = dataset.map(map_to_discrete_units)

print(dataset)

Automatic Speech Recognition

In order to use this model for Speech Recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model for Speech Recognition.

Audio Classification

For an explanation of how to fine-tune the model for Audio Classification, check out this tutorial.

Citation

If this model contributes to your research, please cite the work:

@misc{costa2025hubertbaseca2k,
      title={LOSHuBERT: the first full Iberian pre-trained HuBERT.}, 
      author={Costa, Federico; Messaoudi, Abir; Peiró-Lilja, Alex; Casals-Salvador, Marc; España-Bonet, Cristina},
      organization={Barcelona Supercomputing Center},
      url={https://huggingface.co/BSC-LT/hubert-base-los-2k},
      year={2025}
}

Additional Information

Author

The pre-training process was performed during 2025, in the Language Technologies Unit of the Barcelona Supercomputing Center.

Contact

For further information, please send an email to langtech@bsc.es.

Copyright

Copyright(c) 2025 by Language Technologies Unit, Barcelona Supercomputing Center.

License

Apache-2.0

Funding

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA.

The training of the model was possible thanks to the computing time provided by Barcelona Supercomputing Center through MareNostrum 5.

Disclaimer

Click to expand

The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.

Be aware that the model may have biases and/or any other undesirable distortions.

When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties.