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--- |
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language: sv-SE |
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datasets: |
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- common_voice |
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- NST Swedish ASR Database |
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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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- voxpopuli |
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license: cc-by-nc-4.0 |
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model-index: |
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- name: Wav2vec 2.0 base VoxPopuli-sv 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: NST Swedish ASR Database |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 5.619804368919309 |
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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: 19.145252414798616 |
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--- |
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# Wav2vec 2.0 base-voxpopuli-sv-swedish |
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Finetuned version of Facebooks [VoxPopuli-sv base](https://huggingface.co/facebook/wav2vec2-base-sv-voxpopuli) model using 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 **5.62%**, WER for Common Voice test set is **19.15%**. |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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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-base-voxpopuli-sv-swedish") |
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model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-base-voxpopuli-sv-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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``` |
