docs: Update model card
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README.md
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name: WER
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---
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This is a
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This repository contains a Wav2Vec2 model trained on the [CoRal-v2 dataset](https://huggingface.co/datasets/CoRal-project/coral-v2/tree/main). The CoRal-v2 dataset includes a rich variety of Danish conversational and read-aloud data, distributed across diverse age groups, genders, and dialects. The model is designed for automatic speech recognition (ASR).
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## Quick Start
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Explore the following audio samples along with their transcriptions and accuracy metrics. Each example showcases the model's performance with different Danish dialects.
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*det blev til
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**
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---
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## Model Details
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Wav2Vec2 is a state-of-the-art model architecture for speech recognition, leveraging self-supervised learning from raw audio data. The pre-trained [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m) has been fine-tuned for automatic speech recognition with the [CoRal-v2 dataset](https://huggingface.co/datasets/CoRal-project/coral-v2/tree/main) dataset to enhance its performance in recognizing Danish speech with consideration to different dialects. The model was trained for 30K steps using the training setup in the [CoRaL repository](https://github.com/alexandrainst/coral/tree) by running:
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```
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python src/scripts/finetune_asr_model.py model=wav2vec2-small max_steps=30000 datasets.coral_conversation_internal.id=CoRal-project/coral-v2 datasets.coral_readaloud_internal.id=CoRal-project/coral-v2
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```
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The model is evaluated using a Language Model (LM) as post-processing. The utilized LM is the one trained and used by [CoRal-project/roest-wav2vec2-315m-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2).
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- Read-aloud
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- **Language**: Danish.
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- **Variation**: Includes various dialects, age groups, and gender distinctions.
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### License
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Note that the dataset used is licensed under a custom license, adapted from OpenRAIL-M, which allows commercial use with a few restrictions (speech synthesis and biometric identification). See [license](https://huggingface.co/Alvenir/coral-1-whisper-large/blob/main/LICENSE).
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---
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## Evaluation
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The model was evaluated using the following metrics:
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- **Word Error Rate (WER)**: The percentage of words incorrectly transcribed.
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- **Character Error Rate (CER)**: The percentage of characters incorrectly transcribed.
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| Model | Number of parameters | Finetuned on data of type | [CoRal](https://huggingface.co/datasets/CoRal-project/coral/viewer/read_aloud/test) CER | [CoRal](https://huggingface.co/datasets/CoRal-project/coral/viewer/read_aloud/test) WER |
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| :----------------------------------------------------------------------------------------------- | -------------------: | --------------------------: | --------------------------------------------------------------------------------------: | --------------------------------------------------------------------------------------: |
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| [CoRal-project/roest-wav2vec2-1B-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-1B-v2) | 1B | Read-aloud and conversation | 6.5% ± 0.2% | 16.4% ± 0.4% |
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| [CoRal-project/roest-wav2vec2-315M-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2) | 315M | Read-aloud and conversation | 6.5% ± 0.2% | 16.3% ± 0.4% |
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| [CoRal-project/roest-whisper-large-v1](https://huggingface.co/CoRal-project/roest-whisper-large-v1) | 1540M | Read-aloud | **4.3% ± 0.2%** | **10.4% ± 0.3%** |
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| [CoRal-project/roest-wav2vec2-315M-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315M-v1) | 315M | Read-aloud | 6.6% ± 0.2% | 17.0% ± 0.4% |
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| [mhenrichsen/hviske-v2](https://huggingface.co/syvai/hviske-v2) | 1540M | Read-aloud | 4.7% ± 0.2% | 11.8% ± 0.3% |
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| [openai/whisper-large-v3](https://hf.co/openai/whisper-large-v3) | 1540M | - | 11.4% ± 0.3% | 28.3% ± 0.6% |
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| Model | Number of parameters | Finetuned on data of type | [CoRal-v2::conversation](https://huggingface.co/datasets/CoRal-project/coral-v2/viewer/conversation/test) CER | [CoRal-v2::conversation](https://huggingface.co/datasets/CoRal-project/coral-v2/viewer/conversation/test) WER |
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| :-------------------------------------------------------------------------------------------------- | -------------------: | --------------------------: | ------------------------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------: |
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| [CoRal-project/roest-wav2vec2-1B-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-1B-v2) | 1B | Read-aloud and conversation |
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| [CoRal-project/roest-wav2vec2-315M-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2) | 315M | Read-aloud and conversation | 24.2% | 37.7% |
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| [CoRal-project/roest-whisper-large-v1](https://huggingface.co/CoRal-project/roest-whisper-large-v1)
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| [CoRal-project/roest-wav2vec2-315m-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1)
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<img src="https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2/resolve/main/images/cer.png">
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### Performance on Other Datasets
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The model was also tested against other datasets to evaluate generalizability:
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| ------------------------------------------------------------------------------------- | -------------------------- |
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| Evaluation Dataset
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| [CoRal](https://huggingface.co/datasets/CoRal-project/coral/viewer/read_aloud/test) | **10.4** | **4.3**
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| [NST-da](https://huggingface.co/datasets/alexandrainst/nst-da) | 29.8 | 14.5
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| [CommonVoice17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) | 15.6 | 8.2
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| [Fleurs-da_dk](https://huggingface.co/datasets/google/fleurs) | **12.6** | **5.1**
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**OBS!** The vocab used for training incudes numerals (0,1,2,..,9), which are translated to text in a post-processing step. If the model misses spaces the numbers are interpreted as one, which especially affects the NST score as this dataset contains many numerals.
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---
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### Note on comparing whisper and wav2vec2 models
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The Whisper models detailed in this model card exhibit significantly lower Character Error Rates (CER) and Word Error Rates (WER) compared to the Wav2Vec2 models. Whisper utilizes a transformer-based architecture with additional layers that enhance contextual understanding. In contrast, Wav2Vec2 models employ shorter context windows that focus on sound prediction. The Roest-Wav2Vec2 models incorporate a straightforward language model during post-processing, which addresses errors based on statistical language patterns. Introducing a more complex, contextual post-processing language model might enable a better comparison between these model types, which the CoRal project plans to explore in future releases.
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---
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We would like specifically to thank Dan Saattrup Nielsen, Alexandra Institute for (among other things) the repository work and Simon Leminen Madsen, Alexandra Institute for modelling work.
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## Citation
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```bibtex
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@misc{roest-wav2vec2-315m-v2,
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author = {Marie Juhl Jørgensen, Søren Vejlgaard Holm, Martin Carsten Nielsen, Dan Saattrup Nielsen, Sif Bernstorff Lehmann, Simon Leminen Madsen and Torben Blach},
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title = {
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year = {2025},
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url = {https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2},
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}
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name: WER
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# Røst-wav2vec2-315m-v2
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This is a Danish state-of-the-art speech recognition model, trained as part of the CoRal project by [Alvenir](https://www.alvenir.ai/).
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This repository contains a Wav2Vec2 model trained on the [CoRal-v2 dataset](https://huggingface.co/datasets/CoRal-project/coral-v2/tree/main).
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The CoRal-v2 dataset includes a rich variety of Danish conversational and read-aloud data, distributed across diverse age groups, genders, and dialects.
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The model is designed for automatic speech recognition (ASR).
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## Quick Start
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Explore the following audio samples along with their transcriptions and accuracy metrics. Each example showcases the model's performance with different Danish dialects.
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<details>
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<summary>
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<b>Example 1 - Vestjysk Dialect</b>
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</summary>
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**Audio Sample:**
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<audio controls>
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<source src="https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2/resolve/main/audio_samples/example1.wav" type="audio/wav">
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Your browser does not support the audio tag.
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</audio>
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**Model Transcription:**
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*det blev til yderlig ti mål i den første sæson på trods af en position som back*
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**Target Transcription:**
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*det blev til yderligere ti mål i den første sæson på trods af en position som back*
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- **Character Error Rate (CER):** 3.7%
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- **Word Error Rate (WER):** 5.9%
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</details>
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<details>
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<summary>
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<b>Example 2 - Sønderjysk Dialect</b>
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</summary>
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**Audio Sample:**
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<audio controls>
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<source src="https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2/resolve/main/audio_samples/example2.wav" type="audio/wav">
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Your browser does not support the audio tag.
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</audio>
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**Model Transcription:**
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*en arkitektoniske udformning af pladser forslagene iver benzen*
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**Target Transcription:**
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*den arkitektoniske udformning af pladsen er forestået af ivar bentsen*
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- **Character Error Rate (CER):** 20.3%
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- **Word Error Rate (WER):** 60.0%
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</details>
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<details>
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<summary>
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<b>Example 3 - Nordsjællandsk Dialect</b>
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</summary>
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**Audio Sample:**
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<audio controls>
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<source src="https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2/resolve/main/audio_samples/example3.wav" type="audio/wav">
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Your browser does not support the audio tag.
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</audio>
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**Model Transcription:**
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*østrig og ungarn samarbejder om søen gennem den østrigske og ungarske vandkommission*
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**Target Transcription:**
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*østrig og ungarn samarbejder om søen gennem den østrigske og ungarske vandkommission*
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- **Character Error Rate (CER):** 0.0%
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- **Word Error Rate (WER):** 0.0%
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</details>
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<details>
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<summary>
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<b>Example 4 - Lollandsk Dialect</b>
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</summary>
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**Audio Sample:**
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<audio controls>
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<source src="https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2/resolve/main/audio_samples/example4.wav" type="audio/wav">
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Your browser does not support the audio tag.
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</audio>
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**Model Transcription:**
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*det er produceret af thomas helme og indspillede i easy sound recording studio i københavn*
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**Target Transcription:**
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*det er produceret af thomas helmig og indspillet i easy sound recording studio i københavn*
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- **Character Error Rate (CER):** 4.4%
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- **Word Error Rate (WER):** 13.3%
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</details>
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---
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## Model Details
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Wav2Vec2 is a state-of-the-art model architecture for speech recognition, leveraging self-supervised learning from raw audio data. The pre-trained [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m) has been fine-tuned for automatic speech recognition with the [CoRal-v2 dataset](https://huggingface.co/datasets/CoRal-project/coral-v2/tree/main) dataset to enhance its performance in recognizing Danish speech with consideration to different dialects. The model was trained for 30K steps using the training setup in the [CoRaL repository](https://github.com/alexandrainst/coral/tree) by running:
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```bash
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python src/scripts/finetune_asr_model.py \
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model=wav2vec2-small \
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max_steps=30000 \
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datasets.coral_conversation_internal.id=CoRal-project/coral-v2 \
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datasets.coral_readaloud_internal.id=CoRal-project/coral-v2
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```
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The model is evaluated using a Language Model (LM) as post-processing.
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The utilized LM is the one trained and used by [CoRal-project/roest-wav2vec2-315m-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1).
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The model was trained on the [CoRal-v2](https://huggingface.co/datasets/CoRal-project/coral-v2/tree/main) dataset, including both the conversational and read-aloud subset.
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This dataset consists of Danish speech across a variety of dialects, age groups and gender distinctions.
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Note that the dataset, and thus also this model, is licensed under a custom license, adapted from OpenRAIL-M, which allows commercial use with few restrictions (speech synthesis and biometric identification) - see [license](https://huggingface.co/Alvenir/coral-1-whisper-large/blob/main/LICENSE).
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---
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## Evaluation
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The model was evaluated using the following metrics:
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- **Character Error Rate (CER)**: The percentage of characters incorrectly transcribed.
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- **Word Error Rate (WER)**: The percentage of words incorrectly transcribed.
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### Conversational CoRal Performance
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The model was firstly evaluated on a tentative version of the coral-v2 conversation dataset.
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The results are tentative as the test set only includes 5 unique speakers, of which 4 are women.
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The test set includes 2 speakers with 'Fynsk' dialect, 1 with 'Sønderjysk', 1 with 'Non-native' and 1 'Nordjysk'.
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The Whisper model is performing very poorly on the test set. An explanation could be hallucinations during silence and short sentences, a known whisper issue.
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Furthermore, both v1 models have not been trained on any conversation data, giving the models an obvious disadvantage.
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| Model | Number of parameters | Finetuned on data of type | [CoRal-v2::conversation](https://huggingface.co/datasets/CoRal-project/coral-v2/viewer/conversation/test) CER | [CoRal-v2::conversation](https://huggingface.co/datasets/CoRal-project/coral-v2/viewer/conversation/test) WER |
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| :-------------------------------------------------------------------------------------------------- | -------------------: | --------------------------: | ------------------------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------: |
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| [CoRal-project/roest-wav2vec2-1B-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-1B-v2) | 1B | Read-aloud and conversation | **23.9%** | **36.7%** |
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| [CoRal-project/roest-wav2vec2-315M-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2) | 315M | Read-aloud and conversation | 24.2% | 37.7% |
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| [CoRal-project/roest-whisper-large-v1](https://huggingface.co/CoRal-project/roest-whisper-large-v1) | 1540M | Read-aloud | 138% | 121% |
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| [CoRal-project/roest-wav2vec2-315m-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1) | 315M | Read-aloud | 123% | 80.5% |
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### Read-aloud CoRal Performance
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| Model | Number of parameters | Finetuned on data of type | [CoRal](https://huggingface.co/datasets/CoRal-project/coral/viewer/read_aloud/test) CER | [CoRal](https://huggingface.co/datasets/CoRal-project/coral/viewer/read_aloud/test) WER |
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| :-------------------------------------------------------------------------------------------------- | -------------------: | --------------------------: | --------------------------------------------------------------------------------------: | --------------------------------------------------------------------------------------: |
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| [CoRal-project/roest-wav2vec2-1B-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-1B-v2) | 1B | Read-aloud and conversation | 6.5% ± 0.2% | 16.4% ± 0.4% |
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| [CoRal-project/roest-wav2vec2-315M-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2) | 315M | Read-aloud and conversation | 6.5% ± 0.2% | 16.3% ± 0.4% |
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| [CoRal-project/roest-whisper-large-v1](https://huggingface.co/CoRal-project/roest-whisper-large-v1) | 1540M | Read-aloud | **4.3% ± 0.2%** | **10.4% ± 0.3%** |
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| [CoRal-project/roest-wav2vec2-315M-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315M-v1) | 315M | Read-aloud | 6.6% ± 0.2% | 17.0% ± 0.4% |
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| [mhenrichsen/hviske-v2](https://huggingface.co/syvai/hviske-v2) | 1540M | Read-aloud | 4.7% ± 0.2% | 11.8% ± 0.3% |
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| [openai/whisper-large-v3](https://hf.co/openai/whisper-large-v3) | 1540M | - | 11.4% ± 0.3% | 28.3% ± 0.6% |
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+
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**OBS!** Benchmark for hviske-v2 has been re-evaluated and the confidence interval is larger than reported in the model card.
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<img src="https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2/resolve/main/images/cer.png">
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<img src="https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2/resolve/main/images/wer.png">
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+
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<details>
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<summary>
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<b>Detailed CER scores in % of evaluation across demographics on the CoRal test data</b>
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</summary>
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| Category | Røst-whisper-large-v1 | Røst-wav2vec2-315m-v1 | Røst-wav2vec2-315m-v2 | Røst-wav2vec2-1B-v2 |
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|:---:|:---:|:---:|:---:|:---:|
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| female | 5.1 | 7.4 | 7.2 | 7.3 |
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| male | 3.6 | 5.8 | 5.7 | 5.8 |
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| 0-25 | 3.4 | 5.4 | 5.3 | 5.1 |
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| 25-50 | 4.0 | 6.2 | 6.0 | 5.7 |
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| 50+ | 5.0 | 7.5 | 7.4 | 7.8 |
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| Bornholmsk | 3.8 | 6.8 | 6.1 | 6.2 |
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| Fynsk | 5.1 | 7.4 | 7.2 | 6.9 |
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| Københavnsk | 1.9 | 3.3 | 3.2 | 3.0 |
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| Non-native | 4.8 | 7.8 | 7.5 | 7.3 |
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| Nordjysk | 1.6 | 2.6 | 2.8 | 2.6 |
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| Sjællandsk | 3.0 | 4.4 | 4.5 | 3.9 |
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| Sydømål | 4.1 | 6.4 | 6.4 | 6.5 |
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| Sønderjysk | 8.8 | 11.9 | 11.6 | 12.6 |
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| Vestjysk | 6.4 | 10.1 | 9.8 | 10.5 |
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| Østjysk | 2.6 | 4.0 | 4.1 | 3.8 |
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| Overall | 4.3 | 6.6 | 6.5 | 6.5 |
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+
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+
</details>
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+
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+
<details>
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<summary>
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+
<b>Detailed WER scores in % of evaluation across demographics on the CoRal test data</b>
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</summary>
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+
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+
| Category | Røst-whisper-large-v1 | Røst-wav2vec2-315m-v1 | Røst-wav2vec2-315m-v2 | Røst-wav2vec2-1B-v2 |
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| 245 |
+
|:---:|:---:|:---:|:---:|:---:|
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+
| female | 11.5 | 18.5 | 17.7 | 17.8 |
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| male | 9.4 | 15.5 | 14.9 | 15.0 |
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| 0-25 | 9.0 | 14.7 | 14.0 | 13.7 |
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| 25-50 | 10.1 | 16.6 | 15.8 | 15.3 |
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| 50+ | 11.3 | 18.2 | 17.7 | 18.5 |
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| Bornholmsk | 9.8 | 17.7 | 15.7 | 16.4 |
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| Fynsk | 12.1 | 18.3 | 17.7 | 16.7 |
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| Københavnsk | 5.9 | 10.2 | 10.0 | 9.5 |
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| Non-native | 12.2 | 20.9 | 19.4 | 19.4 |
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| Nordjysk | 4.5 | 7.7 | 7.5 | 7.3 |
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| Sjællandsk | 7.6 | 12.6 | 12.7 | 11.0 |
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| Sydømål | 10.0 | 14.9 | 15.3 | 14.4 |
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| Sønderjysk | 17.5 | 26.0 | 25.4 | 27.8 |
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| Vestjysk | 15.0 | 26.3 | 25.2 | 26.7 |
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| Østjysk | 7.5 | 11.7 | 11.3 | 10.8 |
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| Overall | 10.4 | 17.0 | 16.3 | 16.4 |
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| 262 |
+
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| 263 |
+
</details>
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| 264 |
+
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| 265 |
+
<details>
|
| 266 |
+
<summary>
|
| 267 |
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<b>Experiments with Røst-wav2vec2-315M with and without language model</b>
|
| 268 |
+
</summary>
|
| 269 |
+
|
| 270 |
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The inclusion of a post-processing language model can affect the performance significantly.
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| 271 |
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The Røst-v1 and Røst-v2 models are using the same Language Model (LM).
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| 272 |
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The utilized LM is the one trained and used by [CoRal-project/roest-wav2vec2-315m-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1).
|
| 273 |
+
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| 274 |
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| Model | Number of parameters | Finetuned on data of type | Postprocessed with Language Model | [CoRal](https://huggingface.co/datasets/alexandrainst/coral/viewer/read_aloud/test) CER | [CoRal](https://huggingface.com/datasets/alexandrainst/coral/viewer/read_aloud/test) WER |
|
| 275 |
+
| :-------------------------------------------------------------------------------------------------- | -------------------: | --------------------------: | --------------------------------: | --------------------------------------------------------------------------------------: | ---------------------------------------------------------------------------------------: |
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| 276 |
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| [CoRal-project/roest-wav2vec2-1B-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-1B-v2) | 1B | Read-aloud and conversation | Yes | **6.5% ± 0.2%** | **16.4% ± 0.4%** |
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| 277 |
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| [CoRal-project/roest-wav2vec2-1B-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-1B-v2) | 1B | Read-aloud and conversation | No | 8.1% ± 0.2% | 23.9% ± 0.4% |
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| 278 |
+
| [CoRal-project/roest-wav2vec2-315M-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2) | 315M | Read-aloud and conversation | Yes | **6.5% ± 0.2%** | **16.3% ± 0.4%** |
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| 279 |
+
| [CoRal-project/roest-wav2vec2-315M-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2) | 315M | Read-aloud and conversation | No | 8.2% ± 0.2% | 25.1% ± 0.4% |
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| 280 |
+
| [CoRal-project/roest-wav2vec2-315m-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1) | 315M | Read-aloud | Yes | 6.6% ± 0.2% | 17.0% ± 0.4% |
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| 281 |
+
| [CoRal-project/roest-wav2vec2-315m-v1](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1) | 315M | Read-aloud | No | 8.6% ± 0.2% | 26.3% ± 0.5% |
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| 282 |
+
|
| 283 |
+
Here are the results of the model on different Danish dialects in the test set:
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| 284 |
+
|
| 285 |
+
| | Røst-v1 | | Røst-v1 | | Røst-v2 | | Røst-v2 | |
|
| 286 |
+
|-------------|---------|---------|---------|---------|---------|---------|---------|---------|
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| 287 |
+
| **LM** | **No** | | **Yes** | | **No** | | **Yes** | |
|
| 288 |
+
|-------------|---------|---------|---------|---------|---------|---------|---------|---------|
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| 289 |
+
| Dialect | CER (%) | WER (%) | CER (%) | WER (%) | CER (%) | WER (%) | CER (%) | WER (%) |
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| 290 |
+
| Vestjysk | 12.7 | 37.1 | 10.1 | 26.3 | 12.2 | 36.3 | 9.82 | 25.2 |
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| 291 |
+
| Sønderjysk | 14.7 | 37.8 | 11.9 | 26.0 | 14.2 | 36.2 | 11.6 | 25.4 |
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| 292 |
+
| Bornholmsk | 9.32 | 29.9 | 6.79 | 17.7 | 8.08 | 26.7 | 6.12 | 15.7 |
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| 293 |
+
| Østjysk | 5.51 | 18.7 | 3.97 | 11.7 | 5.39 | 18.0 | 4.06 | 11.3 |
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| 294 |
+
| Nordjysk | 3.86 | 13.6 | 2.57 | 7.72 | 3.80 | 13.5 | 2.75 | 7.51 |
|
| 295 |
+
| Københavnsk | 5.27 | 18.8 | 3.31 | 10.2 | 5.02 | 17.7 | 3.20 | 9.98 |
|
| 296 |
+
| Fynsk | 9.41 | 28.6 | 7.43 | 18.3 | 8.86 | 27.0 | 7.20 | 17.7 |
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| 297 |
+
| Non-native | 10.6 | 33.2 | 7.84 | 20.9 | 10.0 | 31.6 | 7.46 | 19.4 |
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| 298 |
+
| Sjællandsk | 5.82 | 19.5 | 4.44 | 12.6 | 5.70 | 18.6 | 4.48 | 12.7 |
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| 299 |
+
| Sydømål | 7.09 | 20.7 | 6.38 | 14.9 | 6.96 | 20.4 | 6.44 | 15.3 |
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| 300 |
+
|
| 301 |
+
</details>
|
| 302 |
|
| 303 |
### Performance on Other Datasets
|
| 304 |
|
| 305 |
The model was also tested against other datasets to evaluate generalizability:
|
| 306 |
|
| 307 |
+
| | **Røst-whisper-large-v1** | | **Røst-wav2vec2-315M-v1** | | **Røst-wav2vec2-315M-v2** | | **Røst-wav2vec2-1B-v2** | |
|
| 308 |
+
| ------------------------------------------------------------------------------------- | -------------------------- | --------- | -------------------------- | --------- | -------------------------- | ----------- | ------------------------ | --------- |
|
| 309 |
+
| **Evaluation Dataset** | **WER %** | **CER %** | **WER %** | **CER %** | **WER %** | **CER %** | **WER %** | **CER %** |
|
| 310 |
+
| [CoRal](https://huggingface.co/datasets/CoRal-project/coral/viewer/read_aloud/test) | **10.4** | **4.3** | 17.0 | 6.6 | **16.3** | **6.5** | 16.4 | **6.5** |
|
| 311 |
+
| [NST-da](https://huggingface.co/datasets/alexandrainst/nst-da) | 29.8 | 14.5 | 29.7 | 13.9 | 26.1 | 11.9 | **12.4** | **4.9** |
|
| 312 |
+
| [CommonVoice17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) | 15.6 | 8.2 | 16.7 | 6.6 | **14.4** | **5.4** | 26.3 | 10.9 |
|
| 313 |
+
| [Fleurs-da_dk](https://huggingface.co/datasets/google/fleurs) | **12.6** | **5.1** | 16.6 | 6.3 | 15.6 | 6.1 | **13.7** | **5.5** |
|
| 314 |
|
| 315 |
**OBS!** The vocab used for training incudes numerals (0,1,2,..,9), which are translated to text in a post-processing step. If the model misses spaces the numbers are interpreted as one, which especially affects the NST score as this dataset contains many numerals.
|
| 316 |
|
| 317 |
---
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|
| 318 |
|
| 319 |
+
### Note on comparing Whisper and Wav2Vec2 models
|
| 320 |
+
The Whisper models detailed in this model card exhibit significantly lower Character Error Rates (CER) and Word Error Rates (WER) compared to the Wav2Vec2 models.
|
| 321 |
+
Whisper utilizes a transformer-based architecture with additional layers that enhance contextual understanding.
|
| 322 |
+
In contrast, Wav2Vec2 models employ shorter context windows that focus on sound prediction.
|
| 323 |
+
The Røst-Wav2Vec2 models incorporate a straightforward language model during post-processing, which addresses errors based on statistical language patterns.
|
| 324 |
+
Introducing a more complex, contextual post-processing language model might enable a better comparison between these model types, which the CoRal project plans to explore in future releases.
|
| 325 |
+
|
| 326 |
+
The Røst-Whisper model excels in read-aloud data, leveraging its embedded contextual framework to achieve more robust recognition within this context.
|
| 327 |
+
However, Wav2Vec2 models appear to generalize more effectively across various speech recognition tasks, whereas Whisper models incur higher error rates in conversational data.
|
| 328 |
+
It’s important to note that the CoRal-v2 conversation dataset, being tentative and featuring limited speaker diversity, might influence these results.
|
| 329 |
|
| 330 |
---
|
| 331 |
|
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|
| 347 |
|
| 348 |
We would like specifically to thank Dan Saattrup Nielsen, Alexandra Institute for (among other things) the repository work and Simon Leminen Madsen, Alexandra Institute for modelling work.
|
| 349 |
|
| 350 |
+
|
| 351 |
## Citation
|
| 352 |
|
| 353 |
```bibtex
|
| 354 |
@misc{roest-wav2vec2-315m-v2,
|
| 355 |
author = {Marie Juhl Jørgensen, Søren Vejlgaard Holm, Martin Carsten Nielsen, Dan Saattrup Nielsen, Sif Bernstorff Lehmann, Simon Leminen Madsen and Torben Blach},
|
| 356 |
+
title = {Røst-wav2vec-315m-v2: A Danish state-of-the-art speech recognition model trained on varied demographics and dialects},
|
| 357 |
year = {2025},
|
| 358 |
url = {https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2},
|
| 359 |
}
|