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README.md

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+language: ca
+datasets:
+- common_voice 
+- parlament_parla
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: Catalan XLSR Wav2Vec2 Large
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    datasets:
+      - name: Common Voice ca
+        type: common_voice
+        args: ca
+      - name: ParlamentParla
+        url: https://www.openslr.org/59/
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 7.57
+       - name: Google Crowsourced Corpus WER
+         type: wer
+         value: 
+       - name: Audiobook “La llegenda de Sant Jordi” WER
+         type: wer
+         value: 13.23
+---
+
+# Wav2Vec2-Large-XLSR-Català
+
+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.
+
+**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.
+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.
+
+You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala)
+
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+## Results
+
+Word error rate was evaluated on the following datasets unseen by the model:
+
+| Dataset | WER |
+| ------- | --- |
+| [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv)) | 7.57 |
+| [Google Crowsourced Corpus](https://www.openslr.org/69/) | |
+| Audiobook “La llegenda de Sant Jordi” | 13.23 | 
+
+
+## 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", "ca", split="test[:2%]")
+
+processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala") 
+model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the audio 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])
+```