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--- |
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language: ca |
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datasets: |
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- common_voice |
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- parlament_parla |
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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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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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model-index: |
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- name: Catalan XLSR Wav2Vec2 Large |
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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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datasets: |
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- name: Common Voice ca |
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type: common_voice |
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args: ca |
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- name: ParlamentParla |
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url: https://www.openslr.org/59/ |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 6.92 |
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- name: Google Crowsourced Corpus WER |
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type: wer |
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value: 12.99 |
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- name: Audiobook “La llegenda de Sant Jordi” WER |
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type: wer |
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value: 13.23 |
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--- |
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# Wav2Vec2-Large-XLSR-Català |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets. |
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**Attention:** The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoice and ParlamentParla dataset and can be found [here](https://github.com/ccoreilly/wav2vec2-catala). Evaluating on the CV test dataset will produce a biased WER as 1144 audio files of that dataset were used in training/evaluation of this model. |
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WER was calculated using this [test.csv](https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv) which was not seen by the model during training/evaluation. |
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You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala) |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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## Results |
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Word error rate was evaluated on the following datasets unseen by the model: |
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| Dataset | WER | |
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| ------- | --- | |
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| [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv)) | 6.92% | |
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| [Google Crowsourced Corpus](https://www.openslr.org/69/) | 12.99% | |
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| Audiobook “La llegenda de Sant Jordi” | 13.23% | |
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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", "ca", split="test[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala") |
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model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala") |
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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 audio 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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``` |
