--- language: - gl license: apache-2.0 base_model: openai/whisper-large-v2 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Large-V2 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: 5.987858719646799 --- # Whisper Large-V2 Galician ## Model summary **Whisper Large-V2 Galician** is an automatic speech recognition (ASR) model for **Galician (gl)** speech. It is fine-tuned from [openai/whisper-large-v2] on the **Galician portion of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 5.99%** on the evaluation split. This model is designed 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-v2 * **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, this model leverages Whisper’s multilingual pretraining to deliver state-of-the-art transcription performance for low-resource languages. --- ## Intended use ### Primary use cases * High-accuracy transcription of Galician audio recordings * Offline or batch ASR pipelines * Research and development in Galician ASR * Media, educational, and archival transcription tasks ### Intended users * Researchers working on Galician or low-resource ASR * Developers building Galician speech applications * Academic or institutional users ### Out-of-scope use * Real-time or low-latency ASR without optimization * Speech translation tasks * Safety-critical applications without validation --- ## Limitations and known issues * Performance may degrade on: * Noisy or low-quality recordings * Conversational or spontaneous speech * Accents underrepresented in Common Voice * Transcription errors may still occur under challenging acoustic conditions * Dataset biases from Common Voice may be reflected in outputs Users are encouraged to evaluate the model on their own data before deployment. --- ## 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) | **5.99%** | This demonstrates the expected transcription quality for a large V2 Whisper model fine-tuned for Galician. --- ## 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 * Evaluation batch size: 16 * Gradient accumulation steps: 2 * Total train batch size: 64 * Seed: 42 ### Training results (summary) | Training Loss | Epoch | Step | Validation Loss | WER | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0185 | 4.01 | 1000 | 0.1896 | 6.3569 | | 0.0067 | 9.01 | 2000 | 0.2083 | 6.3862 | | 0.0038 | 14.01 | 3000 | 0.2291 | 6.4621 | | 0.0022 | 19.01 | 4000 | 0.2412 | 6.4794 | | 0.0013 | 24.01 | 5000 | 0.2515 | 6.4673 | | 0.0023 | 29.01 | 6000 | 0.2570 | 6.6432 | | 0.0018 | 34.01 | 7000 | 0.2474 | 6.6380 | | 0.0017 | 39.01 | 8000 | 0.2530 | 6.9312 | | 0.0001 | 44.01 | 9000 | 0.2758 | 6.2379 | | 0.0001 | 49.01 | 10000 | 0.2952 | 6.1241 | | 0.0001 | 54.01 | 11000 | 0.3056 | 6.0499 | | 0.0 | 59.01 | 12000 | 0.3152 | 5.9948 | | 0.0 | 64.01 | 13000 | 0.3244 | 6.0310 | | 0.0 | 69.01 | 14000 | 0.3336 | 6.0586 | | 0.0 | 74.01 | 15000 | 0.3428 | 6.0344 | | 0.0 | 79.01 | 16000 | 0.3518 | 6.0017 | | 0.0 | 84.01 | 17000 | 0.3601 | 5.9879 | | 0.0 | 89.01 | 18000 | 0.3675 | 6.0103 | | 0.0 | 94.01 | 19000 | 0.3729 | 6.0068 | | 0.0 | 99.01 | 20000 | 0.3753 | 6.0172 | --- ## Framework versions - Transformers 4.33.0.dev0 - PyTorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3 --- ## How to use ```python from transformers import pipeline hf_model = "HiTZ/whisper-large-v2-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.