w2v-bert-2.0-luganda-CV-train-validation-7.0
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the Luganda mozilla common voices 7.0 dataset. We use the train and validation set for training and the test set for evaluation. When using this dataset, make sure that the audio has a sampling rate of 16kHz.It achieves the following results on the test set:
- Loss: 0.2282
- Wer: 0.1933
Training and evaluation data
The model was trained on version 7 of the Luganda dataset of Mozilla common voices dataset. We used the train and validation set for training and the test dataset for validation. The training script was adapted from this transformers repo.
Training procedure
We trained the model on a 32 GB V100 GPU for 10 epochs using a learning rate of 5e-05. We used the AdamW optimizer.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
1.1859 | 1.89 | 300 | 0.2854 | 0.2866 |
0.1137 | 3.77 | 600 | 0.2503 | 0.2469 |
0.0712 | 5.66 | 900 | 0.2043 | 0.2092 |
0.0446 | 7.55 | 1200 | 0.2156 | 0.2005 |
0.0269 | 9.43 | 1500 | 0.2282 | 0.1933 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
Usage
import torch
import torchaudio
from datasets import load_dataset
from transformers import AutoModelForCTC, Wav2Vec2BertProcessor
test_dataset = load_dataset("common_voice", "lg", split="test[:10]")
model = AutoModelForCTC.from_pretrained("dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0")
processor = Wav2Vec2BertProcessor.from_pretrained("dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
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["sentence"][:2])
Evaluation
The model can be evaluated as follows on the Luganda test dataset.
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import AutoModelForCTC, Wav2Vec2BertProcessor
import re
test_dataset = load_dataset("common_voice", "lg", split="test")
wer = load_metric("wer")
model = AutoModelForCTC.from_pretrained("dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0").to('cuda')
processor = Wav2Vec2BertProcessor.from_pretrained("dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0")
chars_to_remove_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\»\«]'
test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16_000))
def remove_special_characters(batch):
# remove special characters
batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower()
return batch
test_dataset = test_dataset.map(remove_special_characters)
def prepare_dataset(batch):
audio = batch["audio"]
batch["input_features"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
batch["input_length"] = len(batch["input_features"])
batch["labels"] = processor(text=batch["sentence"]).input_ids
return batch
test_dataset = test_dataset.map(prepare_dataset, remove_columns=test_dataset.column_names)
# Evaluation is carried out with a batch size of 1
def map_to_result(batch):
with torch.no_grad():
input_values = torch.tensor(batch["input_features"], device="cuda").unsqueeze(0)
logits = model(input_values).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_str"] = processor.batch_decode(pred_ids)[0]
batch["text"] = processor.decode(batch["labels"], group_tokens=False)
return batch
results = test_dataset.map(map_to_result)
print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["text"])))
Test Result: 19.33%
- Downloads last month
- 3
Model tree for dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0
Base model
facebook/w2v-bert-2.0