This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the openslr dataset. It achieves the following results on the evaluation set:
- Loss: 0.3281
- Wer: 0.3462
Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py):
- WER: 0.3216977389924633
- CER: 0.08653361193169537
Evaluation results with language model on OpenSLR "test" (self-split 10%) (Running ./eval.py):
- WER: 0.257040856802856
- CER: 0.07025001801282513
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. α¬ααααΈ αͺα‘αΉα
- 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: 5e-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: 1000
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
5.0795 | 5.47 | 400 | 4.4121 | 1.0 |
3.5658 | 10.95 | 800 | 3.5203 | 1.0 |
3.3689 | 16.43 | 1200 | 2.8984 | 0.9996 |
2.01 | 21.91 | 1600 | 1.0041 | 0.7288 |
1.6783 | 27.39 | 2000 | 0.6941 | 0.5989 |
1.527 | 32.87 | 2400 | 0.5599 | 0.5282 |
1.4278 | 38.35 | 2800 | 0.4827 | 0.4806 |
1.3458 | 43.83 | 3200 | 0.4429 | 0.4532 |
1.2893 | 49.31 | 3600 | 0.4156 | 0.4330 |
1.2441 | 54.79 | 4000 | 0.4020 | 0.4040 |
1.188 | 60.27 | 4400 | 0.3777 | 0.3866 |
1.1628 | 65.75 | 4800 | 0.3607 | 0.3858 |
1.1324 | 71.23 | 5200 | 0.3534 | 0.3604 |
1.0969 | 76.71 | 5600 | 0.3428 | 0.3624 |
1.0897 | 82.19 | 6000 | 0.3387 | 0.3567 |
1.0625 | 87.66 | 6400 | 0.3339 | 0.3499 |
1.0601 | 93.15 | 6800 | 0.3288 | 0.3446 |
1.0474 | 98.62 | 7200 | 0.3281 | 0.3462 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
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Evaluation results
- Test WER on OpenSLR kmself-reported25.700
- Test CER on OpenSLR kmself-reported7.030
- Test WER on Robust Speech Event - Dev Dataself-reported25.700
- Test CER on Robust Speech Event - Dev Dataself-reported7.030