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 % | |