--- language: de datasets: - common_voice - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice de type: common_voice args: de metrics: - name: Test WER type: wer value: 15.80 --- # Wav2Vec2-Large-XLSR-53-German Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German 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", "de", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") 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 {language} 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", "de", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]' substitutions = { 'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]', 'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]', 'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]', 'c' : '[\č\ć\ç\с]', 'l' : '[\ł]', 'u' : '[\ú\ū\ứ\ů]', 'und' : '[\&]', 'r' : '[\ř]', 'y' : '[\ý]', 's' : '[\ś\š\ș\ş]', 'i' : '[\ī\ǐ\í\ï\î\ï]', 'z' : '[\ź\ž\ź\ż]', 'n' : '[\ñ\ń\ņ]', 'g' : '[\ğ]', 'ss' : '[\ß]', 't' : '[\ț\ť]', 'd' : '[\ď\đ]', "'": '[\ʿ\་\’\`\´\ʻ\`\‘]', 'p': '\р' } 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() for x in substitutions: batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) 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) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` The model can also be evaluated with in 10% chunks which needs less ressources (to be tested). ``` import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import jiwer lang_id = "de" processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]' substitutions = { 'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]', 'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]', 'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]', 'c' : '[\č\ć\ç\с]', 'l' : '[\ł]', 'u' : '[\ú\ū\ứ\ů]', 'und' : '[\&]', 'r' : '[\ř]', 'y' : '[\ý]', 's' : '[\ś\š\ș\ş]', 'i' : '[\ī\ǐ\í\ï\î\ï]', 'z' : '[\ź\ž\ź\ż]', 'n' : '[\ñ\ń\ņ]', 'g' : '[\ğ]', 'ss' : '[\ß]', 't' : '[\ț\ť]', 'd' : '[\ď\đ]', "'": '[\ʿ\་\’\`\´\ʻ\`\‘]', 'p': '\р' } 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() for x in substitutions: batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch # 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 H, S, D, I = 0, 0, 0, 0 for i in range(10): print("test["+str(10*i)+"%:"+str(10*(i+1))+"%]") test_dataset = load_dataset("common_voice", "de", split="test["+str(10*i)+"%:"+str(10*(i+1))+"%]") test_dataset = test_dataset.map(speech_file_to_array_fn) result = test_dataset.map(evaluate, batched=True, batch_size=8) predictions = result["pred_strings"] targets = result["sentence"] chunk_metrics = jiwer.compute_measures(targets, predictions) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] WER = float(S + D + I) / float(H + S + D) print("WER: {:2f}".format(WER*100)) ``` **Test Result**: 15.80 % ## Training The first 50% of the Common Voice `train`, and 12% of the `validation` datasets were used for training (30 epochs on first 12% and 3 epochs on the remainder).