transformers safetensors wav2vec2-bert automatic-speech-recognition generated_from_trainer dataset:common_voice_7_0 base_model:facebook/w2v-bert-2.0 license:mit model-index endpoints_compatible region:us
---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_7_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-luganda-CV-train-validation-7.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_7_0
      type: common_voice_7_0
      config: lg
      split: test
      args: lg
    metrics:
    - name: Wer
      type: wer
      value: 0.1933150003273751
---

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# w2v-bert-2.0-luganda-CV-train-validation-7.0

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/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](https://github.com/MusinguziDenis/Luganda-ASR/blob/main/wav2vec/notebook/Fine_Tune_W2V2_BERT_on_CV7_Luganda.ipynb) was adapted from this [transformers repo](https://huggingface.co/blog/fine-tune-w2v2-bert).

## 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
```python
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.

```python
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%

