wav2vec2-xls-r-1b-khmer

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the openslr dataset. It achieves the following results on the evaluation set:

• Loss: 0.4239
• Wer: 0.4221

Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py):

• WER: 0.4490281634272114
• CER: 0.12198285179047481

Evaluation results on OpenSLR "test" with LM ngram (self-split 10%) (Running ./eval.py):

• WER: 0.32130107100357
• CER: 0.09345053678218891

Note

• Since this dataset is small (4 hours of voice recording), we decided not to train that for too long to avoid overfitting and under-generalization.
• This model performs worse than its 300M-variant. Probably, we don't explore the hyper-parameter enough?

Installation

Install the following libraries on top of HuggingFace Transformers for the supports of language model.

pip install pyctcdecode
pip install https://github.com/kpu/kenlm/archive/master.zip

Usage

Approach 1: Using HuggingFace's pipeline, this will cover everything end-to-end from raw audio input to text output.

from transformers import pipeline

# Load the model
pipe = pipeline(model="vitouphy/wav2vec2-xls-r-300m-khmer")

# Process raw audio
output = pipe("sound_file.wav", chunk_length_s=10, stride_length_s=(4, 2))

Approach 2: More custom way to predict phonemes.

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC 
import librosa
import torch

# load model and processor
processor = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-khmer")
model = Wav2Vec2ForCTC.from_pretrained("vitouphy/wav2vec2-xls-r-300m-khmer")

# Read and process the input
speech_array, sampling_rate = librosa.load("sound_file.wav", sr=16_000)
inputs = processor(speech_array, 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, axis=-1)      
predicted_sentences = processor.batch_decode(predicted_ids)
print(predicted_sentences)

Intended uses & limitations

The data used for this model is only around 4 hours of recordings.

• We split into 80/10/10. Hence, the training hour is 3.2 hours, which is very very small.
• Yet, its performance is not too bad. Quite interesting for such small dataset, actually. You can try it out.
• Its limitation is:
  • Rare characters, e.g. ឬស្សី ឪឡឹក
  • Speech needs to be clear and articulate.
• More data to cover more vocabulary and character may help improve this system.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2000
• num_epochs: 75
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0