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--- |
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language: lg |
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datasets: |
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- common_voice |
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metrics: |
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- wer |
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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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license: apache-2.0 |
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model-index: |
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- name: Wav2Vec2 Luganda by Indonesian-NLP |
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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 lg |
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type: common_voice |
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args: lg |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 7.53 |
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--- |
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# Automatic Speech Recognition for Luganda |
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This is the model built for the |
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[Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition). |
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It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) |
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model on the [Luganda Common Voice dataset](https://huggingface.co/datasets/common_voice) version 7.0. |
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We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/luganda-asr) to test the model. |
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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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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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test_dataset = load_dataset("common_voice", "lg", split="test[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda") |
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model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda") |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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if "audio" in batch: |
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speech_array = torch.tensor(batch["audio"]["array"]) |
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else: |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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print("Prediction:", processor.batch_decode(predicted_ids)) |
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print("Reference:", test_dataset[:2]["sentence"]) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Indonesian test data of Common Voice. |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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test_dataset = load_dataset("common_voice", "lg", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda") |
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model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda") |
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model.to("cuda") |
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chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"] |
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chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]' |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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def speech_file_to_array_fn(batch): |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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if "audio" in batch: |
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speech_array = torch.tensor(batch["audio"]["array"]) |
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else: |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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def evaluate(batch): |
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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WER without KenLM: 15.38 % |
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WER With KenLM: |
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**Test Result**: 7.53 % |
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## Training |
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The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO |
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The script used for training can be found [here](https://github.com/indonesian-nlp/luganda-asr) |
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