2.53 kB
| --- | |
| language: mt | |
| datasets: | |
| - common_voice | |
| tags: | |
| - audio | |
| - automatic-speech-recognition | |
| - speech | |
| - xlsr-fine-tuning-week | |
| license: apache-2.0 | |
| model-index: | |
| - name: XLSR Wav2Vec2 Maltese by Akash PB | |
| results: | |
| - task: | |
| name: Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Common Voice mt | |
| type: common_voice | |
| args: {lang_id} | |
| metrics: | |
| - name: Test WER | |
| type: wer | |
| value: 29.42 | |
| --- | |
| # Wav2Vec2-Large-XLSR-53-Maltese | |
| Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Maltese using the [Common Voice](https://huggingface.co/datasets/common_voice) | |
| 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 torchaudio | |
| from datasets import load_dataset, load_metric | |
| from transformers import ( | |
| Wav2Vec2ForCTC, | |
| Wav2Vec2Processor, | |
| ) | |
| import torch | |
| import re | |
| import sys | |
| model_name = "Akashpb13/xlsr_maltese_wav2vec2" | |
| device = "cuda" | |
| chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\)\\(\\*)]' | |
| model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) | |
| processor = Wav2Vec2Processor.from_pretrained(model_name) | |
| ds = load_dataset("common_voice", "mt", split="test", data_dir="./cv-corpus-6.1-2020-12-11") | |
| resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) | |
| def map_to_array(batch): | |
| speech, _ = torchaudio.load(batch["path"]) | |
| batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() | |
| batch["sampling_rate"] = resampler.new_freq | |
| batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " | |
| return batch | |
| ds = ds.map(map_to_array) | |
| def map_to_pred(batch): | |
| features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") | |
| input_values = features.input_values.to(device) | |
| attention_mask = features.attention_mask.to(device) | |
| with torch.no_grad(): | |
| logits = model(input_values, attention_mask=attention_mask).logits | |
| pred_ids = torch.argmax(logits, dim=-1) | |
| batch["predicted"] = processor.batch_decode(pred_ids) | |
| batch["target"] = batch["sentence"] | |
| return batch | |
| result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys())) | |
| wer = load_metric("wer") | |
| print(wer.compute(predictions=result["predicted"], references=result["target"])) | |
| ``` | |
| **Test Result**: 29.42 % | |