--- language: - gl license: apache-2.0 base_model: openai/whisper-large tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Large 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: 6.939845474613686 --- # Whisper Large Galician ## Model summary **Whisper Large Galician** is an automatic speech recognition (ASR) model for **Galician (gl)** speech. It is fine-tuned from [openai/whisper-large] on the **Galician portion of Mozilla Common Voice 13.0**, achieving a **Word Error Rate (WER) of 6.94%** on the Common Voice evaluation split. This model provides high-accuracy transcription for large-scale Galician ASR applications. --- ## Model description * **Architecture:** Transformer-based encoder–decoder (Whisper) * **Base model:** openai/whisper-large * **Language:** Galician (gl) * **Task:** Automatic Speech Recognition (ASR) * **Output:** Text transcription in Galician * **Decoding:** Autoregressive sequence-to-sequence decoding The large model leverages Whisper’s multilingual pretraining and is fine-tuned on Galician speech data to deliver high-quality transcription suitable for research, media, and accessibility applications. --- ## 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) | **6.94%** | This reflects the expected performance of a large 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 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0126 | 4.01 | 1000 | 0.2128 | 8.3558 | | 0.0032 | 9.01 | 2000 | 0.2262 | 6.9416 | | 0.0022 | 14.01 | 3000 | 0.2528 | 7.1123 | | 0.0025 | 19.01 | 4000 | 0.2643 | 7.3641 | | 0.0015 | 24.01 | 5000 | 0.2596 | 7.3365 | | 0.0014 | 29.01 | 6000 | 0.2723 | 7.6366 | | 0.0008 | 34.01 | 7000 | 0.2778 | 7.6090 | | 0.0003 | 39.01 | 8000 | 0.2880 | 7.2261 | | 0.0004 | 44.01 | 9000 | 0.2920 | 7.6745 | | 0.0001 | 49.01 | 10000 | 0.2854 | 7.4089 | | 0.0 | 54.01 | 11000 | 0.3027 | 7.4365 | | 0.0 | 59.01 | 12000 | 0.3159 | 7.4055 | | 0.0 | 64.01 | 13000 | 0.3242 | 7.3693 | | 0.0 | 69.01 | 14000 | 0.3312 | 7.3072 | | 0.0 | 74.01 | 15000 | 0.3379 | 7.0226 | | 0.0 | 79.01 | 16000 | 0.3442 | 7.0019 | | 0.0 | 84.01 | 17000 | 0.3500 | 6.9933 | | 0.0 | 89.01 | 18000 | 0.3550 | 6.9605 | | 0.0 | 94.01 | 19000 | 0.3589 | 6.9467 | | 0.0 | 99.01 | 20000 | 0.3605 | 6.9398 | --- ## 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-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.