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README.md ADDED
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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: 7.57
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+ - name: Google Crowsourced Corpus WER
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+ type: wer
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+ value:
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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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+
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+ # Wav2Vec2-Large-XLSR-Català
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+
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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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+
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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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+
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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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+
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+ When using this model, make sure that your speech input is sampled at 16kHz.
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+
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+ ## Results
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+
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+ Word error rate was evaluated on the following datasets unseen by the model:
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+
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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)) | 7.57 |
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+ | [Google Crowsourced Corpus](https://www.openslr.org/69/) | |
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+ | Audiobook “La llegenda de Sant Jordi” | 13.23 |
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+
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+
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+ ## Usage
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+
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+ The model can be used directly (without a language model) as follows:
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+
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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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+
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+ test_dataset = load_dataset("common_voice", "ca", split="test[:2%]")
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+
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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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+
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+ resampler = torchaudio.transforms.Resample(48_000, 16_000)
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+
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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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+
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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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+
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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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+
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+ predicted_ids = torch.argmax(logits, dim=-1)
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+
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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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+ ```