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| --- | |
| language: sv-SE | |
| datasets: | |
| - common_voice | |
| - NST Swedish ASR Database | |
| metrics: | |
| - wer | |
| - cer | |
| tags: | |
| - audio | |
| - automatic-speech-recognition | |
| - speech | |
| - voxpopuli | |
| license: cc-by-nc-4.0 | |
| model-index: | |
| - name: Wav2vec 2.0 large VoxPopuli-sv 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: 10.994764 | |
| - name: Test CER | |
| type: cer | |
| value: 3.946846 | |
| --- | |
| # Wav2vec 2.0 large-voxpopuli-sv-swedish | |
| **PLEASE NOTE that [this](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) model performs better and has a less restrictive license.** | |
| Additionally pretrained and finetuned version of Facebooks [VoxPopuli-sv large](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) 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 **3.95%**. WER for Common Voice test set is **10.99%** directly and **7.82%** with a 4-gram language model. | |
| When using this model, make sure that your speech input is sampled at 16kHz. | |
| ## Training | |
| This model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice 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]. | |
| ## 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-voxpopuli-sv-swedish") | |
| model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-large-voxpopuli-sv-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]) | |
| ``` |