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--- |
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language: is |
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datasets: |
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- malromur |
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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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- example_title: Malromur sample 1608 |
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src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/resolve/main/sample1608.flac |
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- example_title: Malromur sample 3860 |
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src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/resolve/main/sample3860.flac |
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model-index: |
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- name: XLSR Wav2Vec2 Icelandic 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: Malromur is |
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type: malromur |
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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: 09.21 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Icelandic |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Icelandic using [Malromur](https://clarin.is/en/resources/malromur/). 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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!pip install num2words |
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``` |
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**Normalizer** |
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```bash |
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# num2word packages |
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# Original source: https://github.com/savoirfairelinux/num2words |
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!mkdir -p ./num2words |
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!wget -O num2words/__init__.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/__init__.py |
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!wget -O num2words/base.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/base.py |
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!wget -O num2words/compat.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/compat.py |
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!wget -O num2words/currency.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/currency.py |
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!wget -O num2words/lang_EU.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/lang_EU.py |
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!wget -O num2words/lang_IS.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/lang_IS.py |
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!wget -O num2words/utils.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/utils.py |
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# Malromur_test selected based on gender and age |
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!wget -O malromur_test.csv https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/malromur_test.csv |
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# Normalizer |
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!wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/normalizer.py |
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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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from normalizer import Normalizer |
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normalizer = Normalizer(lang="is") |
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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) |
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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-icelandic") |
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model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-icelandic").to(device) |
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dataset = load_dataset("csv", data_files={"test": "./malromur_test.csv"})["test"] |
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dataset = dataset.map( |
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normalizer, |
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fn_kwargs={"do_lastspace_removing": True, "text_key_name": "cleaned_sentence"}, |
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remove_columns=list(set(dataset.column_names) - set(['cleaned_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, batched=True, batch_size=8) |
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max_items = np.random.randint(0, len(result), 20).tolist() |
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for i in max_items: |
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reference, predicted = result["cleaned_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: eða eitthvað annað dýr |
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predicted: eða eitthvað annað dýr |
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--- |
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reference: oddgerður |
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predicted: oddgerður |
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--- |
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reference: eiðný |
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predicted: eiðný |
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--- |
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reference: löndum |
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predicted: löndum |
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--- |
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reference: tileinkaði bróður sínum markið |
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predicted: tileinkaði bróður sínum markið |
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--- |
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reference: þetta er svo mikill hégómi |
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predicted: þetta er svo mikill hégómi |
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--- |
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reference: timarit is |
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predicted: timarit is |
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--- |
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reference: stefna strax upp aftur |
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predicted: stefna strax upp aftur |
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--- |
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reference: brekkuflöt |
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predicted: brekkuflöt |
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--- |
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reference: áætlunarferð frestað vegna veðurs |
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predicted: áætluna ferð frestað vegna veðurs |
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--- |
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reference: sagði af sér vegna kláms |
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predicted: sagði af sér vegni kláms |
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reference: grímúlfur |
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predicted: grímúlgur |
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--- |
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reference: lýsti sig saklausan |
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predicted: lýsti sig saklausan |
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--- |
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reference: belgingur is |
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predicted: belgingur is |
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--- |
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reference: sambía |
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predicted: sambía |
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--- |
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reference: geirastöðum |
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predicted: geirastöðum |
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--- |
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reference: varð tvisvar fyrir eigin bíl |
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predicted: var tvisvar fyrir eigin bíl |
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--- |
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reference: reykjavöllum |
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predicted: reykjavöllum |
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--- |
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reference: miklir menn eru þeir þremenningar |
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predicted: miklir menn eru þeir þremenningar |
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--- |
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reference: handverkoghonnun is |
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predicted: handverkoghonnun is |
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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 test data of Malromur. |
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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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from normalizer import Normalizer |
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normalizer = Normalizer(lang="is") |
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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) |
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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-icelandic") |
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model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-icelandic").to(device) |
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dataset = load_dataset("csv", data_files={"test": "./malromur_test.csv"})["test"] |
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dataset = dataset.map( |
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normalizer, |
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fn_kwargs={"do_lastspace_removing": True, "text_key_name": "cleaned_sentence"}, |
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remove_columns=list(set(dataset.column_names) - set(['cleaned_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, batched=True, batch_size=8) |
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wer = load_metric("wer") |
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print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["cleaned_sentence"]))) |
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``` |
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**Test Result**: |
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- WER: 09.21% |
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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/wav2vec2_large_xlsr_is/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-Icelandic--Vmlldzo2Mjk3ODc?accessToken=j7neoz71mce1fkzt0bch4j0l50witnmme07xe90nvs769kjjtbwneu2wfz3oip16) |
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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_Icelandic_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb) |
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## Questions? |
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Post a Github issue on the [Wav2Vec](https://github.com/m3hrdadfi/wav2vec) repo. |
