README.md

metadata

language: lg
datasets:
  - mozilla-foundation/common_voice_7_0
metrics:
  - wer
tags:
  - audio
  - automatic-speech-recognition
  - common_voice
  - hf-asr-leaderboard
  - lg
  - robust-speech-event
  - 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: 9.332
          - name: Test CER
            type: cer
            value: 1.987
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: lg
        metrics:
          - name: Test WER
            type: wer
            value: 13.844
          - name: Test CER
            type: cer
            value: 2.68

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:

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"])

Evaluation

The model can be evaluated as follows on the Indonesian test data of Common Voice.

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "lg", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda") 
model.to("cuda")

chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"]
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'

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):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    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)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))

WER without KenLM: 15.38 %

WER With KenLM:

Test Result: 7.53 %

Training

The Common Voice train, validation, and ... datasets were used for training as well as ... and ... # TODO

The script used for training can be found here