--- language: - gl license: apache-2.0 base_model: openai/whisper-large-v3 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Large-V3 Galician results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_13_0 gl type: mozilla-foundation/common_voice_13_0 config: gl split: test args: gl metrics: - name: Wer type: wer value: 5.008278145695364 --- # Whisper Large-V3 Galician ## Model summary **Whisper Large-V3 Galician** is an automatic speech recognition (ASR) model for **Galician (gl)** speech. It is fine-tuned from [openai/whisper-large-v3] on the **Galician portion of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 5.01%** on the evaluation split. This model is intended for high-accuracy transcription of Galician audio in research, media, and accessibility applications. --- ## Model description * **Architecture:** Transformer-based encoder–decoder (Whisper) * **Base model:** openai/whisper-large-v3 * **Language:** Galician (gl) * **Task:** Automatic Speech Recognition (ASR) * **Output:** Text transcription in Galician * **Decoding:** Autoregressive sequence-to-sequence decoding Fine-tuned on Galician speech data, leveraging Whisper’s multilingual pretraining for low-resource language transcription. --- ## Intended use ### Primary use cases * High-quality transcription of Galician audio recordings * Offline or batch ASR pipelines * Research and development in Galician ASR * Media, educational, and accessibility tasks ### Intended users * Researchers working on Galician or low-resource ASR * Developers building Galician speech applications * Academic and institutional users ### Out-of-scope use * Real-time or low-latency ASR without optimization * Speech translation tasks * Safety-critical applications without additional validation --- ## Limitations and known issues * Performance may degrade on: * Noisy or low-quality recordings * Conversational or spontaneous speech * Accents underrepresented in Common Voice * Dataset biases may be reflected in outputs * Occasional transcription errors can occur under difficult acoustic conditions --- ## Training and evaluation data ### Training data * **Dataset:** Mozilla Common Voice 13.0 (Galician subset) * **Data type:** Crowd-sourced, read speech * **Preprocessing:** * Audio resampled to 16 kHz * Text normalized using Whisper tokenizer * Filtering of invalid or problematic samples ### Evaluation data * **Dataset:** Mozilla Common Voice 13.0 (Galician evaluation split) * **Metric:** Word Error Rate (WER) --- ## Evaluation results | Metric | Value | | ---------- | ---------- | | WER (eval) | **5.01%** | --- ## Training procedure ### Training hyperparameters * Learning rate: 1e-5 * Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-8) * LR scheduler: Linear * Warmup steps: 500 * Training steps: 20,000 * Train batch size: 32 * Eval batch size: 16 * Gradient accumulation steps: 2 * Total train batch size: 64 * Seed: 42 * Mixed precision: Native AMP ### Training results (summary) | Training Loss | Epoch | Step | Validation Loss | WER | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0176 | 5.0 | 1000 | 0.1563 | 5.2514 | | 0.004 | 10.0 | 2000 | 0.1884 | 5.5653 | | 0.0039 | 15.0 | 3000 | 0.2052 | 5.5377 | | 0.0033 | 20.0 | 4000 | 0.2054 | 5.2997 | | 0.0012 | 25.0 | 5000 | 0.2115 | 5.1031 | | 0.001 | 30.0 | 6000 | 0.2195 | 5.2394 | | 0.001 | 35.0 | 7000 | 0.2257 | 5.3446 | | 0.001 | 40.0 | 8000 | 0.2178 | 5.4015 | | 0.0008 | 45.0 | 9000 | 0.2250 | 5.4705 | | 0.0008 | 50.0 | 10000 | 0.2320 | 5.2946 | | 0.0002 | 55.0 | 11000 | 0.2368 | 5.3515 | | 0.0 | 60.0 | 12000 | 0.2551 | 5.0997 | | 0.0 | 65.0 | 13000 | 0.2634 | 5.0738 | | 0.0 | 70.0 | 14000 | 0.2697 | 5.0359 | | 0.0 | 75.0 | 15000 | 0.2752 | 5.0186 | | 0.0 | 80.0 | 16000 | 0.2804 | 5.0066 | | 0.0 | 85.0 | 17000 | 0.2852 | 4.9859 | | 0.0 | 90.0 | 18000 | 0.2894 | 4.9893 | | 0.0 | 95.0 | 19000 | 0.2927 | 5.0014 | | 0.0 | 100.0 | 20000 | 0.2940 | 5.0083 | --- ## Framework versions - Transformers 4.37.2 - PyTorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 --- ## How to use ```python from transformers import pipeline hf_model = "HiTZ/whisper-large-v3-gl" # replace with actual repo ID device = 0 # set to -1 for CPU pipe = pipeline( task="automatic-speech-recognition", model=hf_model, device=device ) result = pipe("audio.wav") print(result["text"]) ``` --- ## Ethical considerations and risks * This model transcribes speech and may process personal data. * Users should ensure compliance with applicable data protection laws (e.g., GDPR). * The model should not be used for surveillance or non-consensual audio processing. --- ## Citation If you use this model in your research, please cite: ```bibtex @misc{dezuazo2025whisperlmimprovingasrmodels, title={Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages}, author={Xabier de Zuazo and Eva Navas and Ibon Saratxaga and Inma Hernáez Rioja}, year={2025}, eprint={2503.23542}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` Please, check the related paper preprint in [arXiv:2503.23542](https://arxiv.org/abs/2503.23542) for more details. --- ## License This model is available under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0). You are free to use, modify, and distribute this model as long as you credit the original creators. --- ## Contact and attribution * Fine-tuning and evaluation: HiTZ/Aholab - Basque Center for Language Technology * Base model: OpenAI Whisper * Dataset: Mozilla Common Voice For questions or issues, please open an issue in the model repository.