File size: 2,616 Bytes
0b482ae 6392b75 0b482ae 6392b75 ea52116 6392b75 ea52116 6392b75 ea52116 6392b75 0b482ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning
The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. For this, the Wav2Vec-HIYACCENT model was proposed which introduced a new layer to the Novel Facebook Wav2vec to capture the disparity between the baseline model and Nigerian English Speeches. A CTC loss was also inserted on top of the model which adds flexibility to the speech-text alignment. This resulted in over 20% improvement in the performance for NAE.T
Fine-tuned facebook/wav2vec2-large on English using the UISpeech Corpus. When using this model, make sure that your speech input is sampled at 16kHz.
The script used for training can be found here: https://github.com/amceejay/HIYACCENT-NE-Speech-Recognition-System
##Usage: The model can be used directly (without a language model) as follows...
#Using the ASRecognition library:
from asrecognition import ASREngine
asr = ASREngine("fr", model_path="codeceejay/HIYACCENT_Wav2Vec2")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)
##Writing your own inference speech:
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "codeceejay/HIYACCENT_Wav2Vec2"
SAMPLES = 10
#You can use common_voice/timit or Nigerian Accented Speeches can also be found here: https://openslr.org/70/
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["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)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
|