HiTZ/stt_eu_conformer_transducer_large_v2

HiTZ/Aholab's Basque Speech-to-Text model Conformer-Transducer v2

Model Description

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This model transcribes speech in lowercase Basque alphabet including spaces, and was trained on a composite dataset comprising of 771.73 hours of Basque speech. The model was fine-tuned from a pre-trained Spanish stt_es_conformer_transducer_large model using the Nvidia NeMo toolkit. It is an autoregressive "large" variant of Conformer, with around 119 million parameters. See the model architecture section and NeMo documentation for complete architecture details.

Usage

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest PyTorch version.

pip install nemo_toolkit['all']

Transcribing using Python

Clone repository to download the model:

git clone https://huggingface.co/HiTZ/stt_eu_conformer_transducer_large_v2

Given NEMO_MODEL_FILEPATH is the path that points to the downloaded stt_eu_conformer_transducer_large_v2.nemo file.

import nemo.collections.asr as nemo_asr

# Load the model
asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(NEMO_MODEL_FILEPATH)

# Create a list pointing to the audio files
audio = ["audio_1.wav","audio_2.wav", ..., "audio_n.wav"]

# Fix the batch_size to whatever number suits your purpouse
batch_size = 8

# Transcribe the audio files
transcriptions = asr_model.transcribe(audio=audio, batch_size=batch_size)

# Visualize the transcriptions
print(transcriptions)

Input

This model accepts 16000 kHz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Model Architecture

Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding instead of CTC loss. You may find more info on the detail of this model here: Conformer-Transducer Model.

Training

Data preparation

This model has been trained on a composite dataset comprising 771.73 hours of Basque speech that contains:

• The 675.98 hours of the train split from the Composite Corpus EU v2.1:
  • This dataset is composed of three main datasets which are:
    • Mozilla Common voice 18.0
    • Basque Parliament EU
    • OpenSLR EU
• A private dataset composed of 95.75 hours of sesions from a basque radio prgram called Faktoria.

Training procedure

This model was trained starting from the pre-trained Spanish model stt_es_conformer_transducer_large over several hundred of epochs in multiple GPU devices, using the NeMo toolkit [3] The tokenizer for these model was built using the text transcripts of the composite train dataset with this script, with a total of 512 basque language tokens.

Performance

Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding in the following table.

Tokenizer	Vocabulary Size	MCV 18.0 Test	Basque Parliament Test	OpenSLR Test	Faktoria Test	MCV 18.0 Dev	Basque Parliament Dev	OpenSLR Dev	Faktoria Dev	Train Dataset
SentencePiece Unigram	512	2.50	3.78	11.87	9.17	2.28	4.20	11.65	12.81	Composite Dataset (771.73 h)

Limitations

Since this model was trained on almost publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.

Aditional Information

Author

HiTZ Basque Center for Language Technology - Aholab Signal Processing Laboratory, University of the Basque Country UPV/EHU.

Copyright

Copyright (c) 2025 HiTZ Basque Center for Language Technology - Aholab Signal Processing Laboratory, University of the Basque Country UPV/EHU.

Licensing Information

Creative Commons Attribution Non Commercial 4.0

Funding

This project with reference 2022/TL22/00215335 has been parcially funded by the Ministerio de Transformación Digital and by the Plan de Recuperación, Transformación y Resiliencia – Funded by the European Union – NextGenerationEU ILENIA and by the project IkerGaitu funded by the Basque Government. This model was trained at Hyperion, one of the high-performance computing (HPC) systems hosted by the DIPC Supercomputing Center.

References

• [1] Conformer: Convolution-augmented Transformer for Speech Recognition
• [2] Google Sentencepiece Tokenizer
• [3] NVIDIA NeMo Toolkit

Disclaimer

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The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their 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 models (HiTZ Basque Center for Language Technology - Aholab Signal Processing Laboratory, University of the Basque Country UPV/EHU.) be liable for any results arising from the use made by third parties of these models.