Kavuma-Jimmy/Luganda-Speech-to-text-Desktop-Version_1

--- language: lg datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech license: apache-2.0 model-index: - name: Wav2Vec2 Luganda by Indonesian-NLP results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice lg type: common_voice args: lg metrics: - name: Test WER type: wer value: 7.53 --- # Automatic Speech Recognition for Luganda This is the model built for the Mozilla Luganda Automatic Speech Recognition competition. It is a fine-tuned facebook/wav2vec2-large-xlsr-53 model on the Luganda Common Voice dataset version 7.0. We also provide a live demo to test the model. 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", "lg", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda") model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda") 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): if "audio" in batch: speech_array = torch.tensor(batch["audio"]["array"]) else: 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[:2]["speech"], 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[:2]["sentence"]) ``` ## E …