--- language: ca datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Catalan XLSR Wav2Vec Large 53 #TODO: replace {human_readable_name} with a name of your model as it should appear on the leaderboard. It could be something like `Elgeish XLSR Wav2Vec2 Large 53` results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ca type: common_voice args: ca #TODO: metrics: - name: Test WER type: wer value: 8.11 --- # Disclaimer This model was trained on Common Voice 6, if you need a catalan model for ASR, I recommend checking [wav2vec2-xls-r-1b-ca-lm](https://huggingface.co/PereLluis13/wav2vec2-xls-r-1b-ca-lm) which is a 1b model with a LM on top trained on CV8+ with much better performance or [wav2vec2-xls-r-300m-ca-lm](https://huggingface.co/PereLluis13/wav2vec2-xls-r-300m-ca-lm) which has the same size (300m) as this model but trained on CV8+ and the same LM. # Wav2Vec2-Large-XLSR-53-ca Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on catalan using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## 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("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan") model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan") 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]) ``` ## Evaluation The model can be evaluated as follows on the catalan test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ca", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan") model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“]' 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): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() 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) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) import jiwer # Chunk WER computation due to memory issues, taken from https://huggingface.co/pcuenq/wav2vec2-large-xlsr-53-es def chunked_wer(targets, predictions, chunk_size=None): if chunk_size is None: return jiwer.wer(targets, predictions) start = 0 end = chunk_size H, S, D, I = 0, 0, 0, 0 while start < len(targets): chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end]) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] start += chunk_size end += chunk_size return float(S + D + I) / float(H + S + D) print("WER: {:2f}".format(100 * chunked_wer(result["sentence"], result["pred_strings"], chunk_size=4000))) ``` **Test Result**: 8.11 % ## Training The Common Voice `train`, `validation` datasets were used for training. At the second epoch training was halted due to a memory issue, and was continued with lower batch size, but acc. gradient steps were scaled to keep it at 32 batch size during all training. Then the model was trained for an additional 10 epochs where half the male samples were pitched up. The script used for training can be found [here](https://github.com/huggingface/transformers/blob/master/examples/research_projects/wav2vec2/run_common_voice.py). Slight modifications were done in order to speed up the ordering by length during training, which can be found [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/6). Another version trained for catalan can be found [here](https://huggingface.co/ccoreilly/wav2vec2-large-xlsr-catala), which may be better than this one since it was trained with extra data and for longer time. Whoever, since it used different splits that include part of the Common Voice test set, this version can be used to get a baseline on the Common Voice dataset.