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--- |
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language: lt |
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datasets: |
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- common_voice |
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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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widget: |
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- label: Common Voice sample 11 |
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src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lithuanian/resolve/main/sample11.flac |
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- label: Common Voice sample 74 |
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src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lithuanian/resolve/main/sample74.flac |
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model-index: |
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- name: XLSR Wav2Vec2 Lithuanian by Mehrdad Farahani |
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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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dataset: |
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name: Common Voice lt |
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type: common_voice |
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args: lt |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 55.13 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Lithuanian |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Lithuanian using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. |
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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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**Requirements** |
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```bash |
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# requirement packages |
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!pip install git+https://github.com/huggingface/datasets.git |
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!pip install git+https://github.com/huggingface/transformers.git |
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!pip install torchaudio |
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!pip install librosa |
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!pip install jiwer |
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``` |
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**Prediction** |
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```python |
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import librosa |
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import torch |
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import torchaudio |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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from datasets import load_dataset |
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import numpy as np |
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import re |
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import string |
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import IPython.display as ipd |
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chars_to_ignore = [ |
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",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", |
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"#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"', |
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"“", "%", "‘", "�", "–", "…", "_", "”", '“', '„' |
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] |
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chars_to_mapping = { |
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"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", |
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} |
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def multiple_replace(text, chars_to_mapping): |
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pattern = "|".join(map(re.escape, chars_to_mapping.keys())) |
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return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text)) |
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def remove_special_characters(text, chars_to_ignore_regex): |
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text = re.sub(chars_to_ignore_regex, '', text).lower() + " " |
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return text |
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def normalizer(batch, chars_to_ignore, chars_to_mapping): |
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chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" |
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text = batch["sentence"].lower().strip() |
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text = multiple_replace(text, chars_to_mapping) |
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text = remove_special_characters(text, chars_to_ignore_regex) |
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batch["sentence"] = text |
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return batch |
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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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speech_array = speech_array.squeeze().numpy() |
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speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) |
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batch["speech"] = speech_array |
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return batch |
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def predict(batch): |
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features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["predicted"] = processor.batch_decode(pred_ids)[0] |
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return batch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-lithuanian") |
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model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-lithuanian").to(device) |
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dataset = load_dataset("common_voice", "lt", split="test[:1%]") |
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dataset = dataset.map( |
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normalizer, |
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fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, |
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remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) |
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) |
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dataset = dataset.map(speech_file_to_array_fn) |
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result = dataset.map(predict) |
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max_items = np.random.randint(0, len(result), 10).tolist() |
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for i in max_items: |
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reference, predicted = result["sentence"][i], result["predicted"][i] |
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print("reference:", reference) |
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print("predicted:", predicted) |
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print('---') |
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``` |
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**Output:** |
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```text |
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reference: vaistinių augalų rinkimas |
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predicted: vaistiniau augalų rinkimas |
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--- |
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reference: penkias iš jų jie įrašė į kasetę ir pradėjo ieškoti dainininko |
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predicted: penkese iš šių ie ji rašę į kasėtę ir pradėjojos škoti dainininklo |
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--- |
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reference: iki mūsų eros pradžios germanija buvo etniškai mišri |
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predicted: ikimūsų eros pradžios germanija buvo etniškai mišri |
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--- |
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reference: pietrytiniame krante netoli užtvankos įrengtas paplūdimys |
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predicted: pietrytiname klante netoli užtvangos įrengtas paplūdimys |
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--- |
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reference: minta smulkiais bestuburiais |
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predicted: minta smulkiais bestubūriais |
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--- |
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reference: jie gyveno ganykloms tinkamose žemėse tarp miestų visoje vakarų afrikoje |
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predicted: je gyveno gonykloms tinkamase žemėse tarp miestų visojava karų ardykoje |
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--- |
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reference: prefektūra yra kazachstano pasienyje |
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predicted: prefektūrą yra kazahstano pasienyje |
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--- |
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reference: į šiaurę ir pietus nuo kaimo buvusios senovės gyvenvietės |
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predicted: į šiaurė ir pietus nuo kaimo buvusius senovės gyvenvietis |
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--- |
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reference: tai vienintelis lietuvos teritorijoje aptiktas toks vertingas zoologinis radinys |
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predicted: tai vieninteris lietuvos ritorijoje aptiktas toksvirtingas zologinis radinys |
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--- |
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reference: pagrindinis partijos reikalavimas buvo vėl sušaukti steigiamąjį susirinkimą ir įtvirtinti rusijoje demokratiją |
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predicted: pagrindinis partijos reikalavimas buvo vėl sušouktis steigiamajį susirinkimą ir įtvyrtinti ir rusijoje demokratije |
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--- |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Persian (Farsi) test data of Common Voice. |
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```python |
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import librosa |
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import torch |
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import torchaudio |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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from datasets import load_dataset, load_metric |
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import numpy as np |
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import re |
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import string |
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chars_to_ignore = [ |
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",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", |
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"#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"', |
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"“", "%", "‘", "�", "–", "…", "_", "”", '“', '„' |
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] |
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chars_to_mapping = { |
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"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", |
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} |
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def multiple_replace(text, chars_to_mapping): |
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pattern = "|".join(map(re.escape, chars_to_mapping.keys())) |
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return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text)) |
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def remove_special_characters(text, chars_to_ignore_regex): |
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text = re.sub(chars_to_ignore_regex, '', text).lower() + " " |
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return text |
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def normalizer(batch, chars_to_ignore, chars_to_mapping): |
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chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" |
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text = batch["sentence"].lower().strip() |
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text = multiple_replace(text, chars_to_mapping) |
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text = remove_special_characters(text, chars_to_ignore_regex) |
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batch["sentence"] = text |
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return batch |
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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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speech_array = speech_array.squeeze().numpy() |
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speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) |
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batch["speech"] = speech_array |
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return batch |
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def predict(batch): |
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features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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input_values = features.input_values.to(device) |
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attention_mask = features.attention_mask.to(device) |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["predicted"] = processor.batch_decode(pred_ids)[0] |
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return batch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-lithuanian") |
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model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-lithuanian").to(device) |
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dataset = load_dataset("common_voice", "lt", split="test") |
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dataset = dataset.map( |
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normalizer, |
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fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, |
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remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) |
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) |
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dataset = dataset.map(speech_file_to_array_fn) |
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result = dataset.map(predict) |
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wer = load_metric("wer") |
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print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"]))) |
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``` |
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] |
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**Test Result**: |
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- WER: 55.13% |
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## Training & Report |
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The Common Voice `train`, `validation` datasets were used for training. |
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You can see the training states [here](https://wandb.ai/m3hrdadfi/finetuned_wav2vec_xlsr_lithuanian/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Lithuanian--Vmlldzo1NjEyNTk?accessToken=hflswq0fdfz1yux9wqfxkdodpu41oqtsgg7et8dld0x05epqwv5nq7nvlhpp09cs) |
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The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Lithuanian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb) |
