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Model: KBLab/wav2vec2-large-voxrex-swedish Source: Original Platform
2026-05-13 08:52:34 +08:00
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+---
+language: sv
+arxiv: https://arxiv.org/abs/2205.03026
+datasets:
+- common_voice
+- NST_Swedish_ASR_Database
+- P4
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- hf-asr-leaderboard
+license: cc0-1.0
+model-index:
+- name: Wav2vec 2.0 large VoxRex Swedish
+  results:
+  - task:
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice
+      type: common_voice
+      args: sv-SE
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 8.49
+---
+# Wav2vec 2.0 large VoxRex Swedish (C)
+
+Finetuned version of KBs [VoxRex large](https://huggingface.co/KBLab/wav2vec2-large-voxrex) model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is **2.5%**. WER for Common Voice test set is **8.49%** directly and **7.37%** with a 4-gram language model.
+
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+**Update 2022-01-10:** Updated to VoxRex-C version.
+
+**Update 2022-05-16:** Paper is is [here](https://arxiv.org/abs/2205.03026).
+
+# Performance\*
+
+![Comparison](comparison.png "Comparison")
+<center><del>*<i>Chart shows performance without the additional 20k steps of Common Voice fine-tuning</i></del></center>
+
+## Training
+This model has been fine-tuned for 120000 updates on NST + CommonVoice<del> and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed]</del>.
+
+![WER during training](chart_1.svg "WER")
+
+## Usage
+The model can be used directly (without a language model) as follows:
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]").
+processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-large-voxrex-swedish")
+model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-large-voxrex-swedish")
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    return batch
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
+with torch.no_grad():
+    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+predicted_ids = torch.argmax(logits, dim=-1)
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset["sentence"][:2])
+```
+
+## Citation
+
+https://arxiv.org/abs/2205.03026
+
+```
+@inproceedings{malmsten2022hearing,
+  title={Hearing voices at the national library : a speech corpus and acoustic model for the Swedish language},
+  author={Malmsten, Martin and Haffenden, Chris and B{\"o}rjeson, Love},
+  booktitle={Proceeding of Fonetik 2022 : Speech, Music and Hearing Quarterly Progress and Status Report, TMH-QPSR},
+  volume={3},
+  year={2022}
+}
+```