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--- |
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language: sv |
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arxiv: https://arxiv.org/abs/2205.03026 |
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datasets: |
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- common_voice |
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- NST_Swedish_ASR_Database |
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- P4 |
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metrics: |
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- wer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- hf-asr-leaderboard |
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license: cc0-1.0 |
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model-index: |
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- name: Wav2vec 2.0 large VoxRex Swedish |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice |
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type: common_voice |
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args: sv-SE |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 8.49 |
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--- |
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# Wav2vec 2.0 large VoxRex Swedish (C) |
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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. |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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**Update 2022-01-10:** Updated to VoxRex-C version. |
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**Update 2022-05-16:** Paper is is [here](https://arxiv.org/abs/2205.03026). |
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# Performance\* |
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 |
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<center><del>*<i>Chart shows performance without the additional 20k steps of Common Voice fine-tuning</i></del></center> |
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## Training |
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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>. |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]"). |
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processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-large-voxrex-swedish") |
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model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-large-voxrex-swedish") |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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print("Prediction:", processor.batch_decode(predicted_ids)) |
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print("Reference:", test_dataset["sentence"][:2]) |
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``` |
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## Citation |
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https://arxiv.org/abs/2205.03026 |
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``` |
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@misc{malmsten2022hearing, |
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title={Hearing voices at the National Library -- a speech corpus and acoustic model for the Swedish language}, |
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author={Martin Malmsten and Chris Haffenden and Love Börjeson}, |
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year={2022}, |
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eprint={2205.03026}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
