89 lines
3.3 KiB
Markdown
89 lines
3.3 KiB
Markdown
---
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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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<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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@inproceedings{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={Malmsten, Martin and Haffenden, Chris and B{\"o}rjeson, Love},
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booktitle={Proceeding of Fonetik 2022 : Speech, Music and Hearing Quarterly Progress and Status Report, TMH-QPSR},
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volume={3},
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year={2022}
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}
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``` |