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---
language:
- km
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- openslr
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Khmer Spaced - Seanghay Yath
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Google FLEURS
type: google/fleurs
config: km_kh
split: all
metrics:
- name: Wer
type: wer
value: 0.6464
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-khmer
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4657
- Wer: 0.6464
## Model description
This model is fine-tuned with Google FLEURS & OpenSLR (SLR42) dataset.
- [ggml-model.bin](https://huggingface.co/seanghay/whisper-small-khmer/blob/main/ggml-model.bin)
- [model.onnx](https://huggingface.co/seanghay/whisper-small-khmer/blob/main/model.onnx)
```python
from transformers import pipeline
pipe = pipeline(
task="automatic-speech-recognition",
model="seanghay/whisper-small-khmer",
)
result = pipe("audio.wav",
generate_kwargs={
"language":"<|km|>",
"task":"transcribe"},
batch_size=16
)
print(result["text"])
```
## whisper.cpp
### 1. Transcode the input audio to 16kHz PCM
```shell
ffmpeg -i audio.ogg -ar 16000 -ac 1 -c:a pcm_s16le output.wav
```
### 2. Transcribe with whisper.cpp
```shell
./main -m ggml-model.bin -f output.wav --print-colors --language km
```
## Training and evaluation data
- `training` = google/fleurs['train+validation'] + openslr['train']
- `eval` = google/fleurs['test']
## Training procedure
This model was trained based on the project on [GitHub](https://github.com/seanghay/whisper-tiny-khmer) with an NVIDIA A10 24GB.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.25e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2065 | 3.37 | 1000 | 0.3403 | 0.7929 |
| 0.0446 | 6.73 | 2000 | 0.2911 | 0.6961 |
| 0.008 | 10.1 | 3000 | 0.3578 | 0.6627 |
| 0.003 | 13.47 | 4000 | 0.3982 | 0.6564 |
| 0.0012 | 16.84 | 5000 | 0.4287 | 0.6512 |
| 0.0004 | 20.2 | 6000 | 0.4499 | 0.6419 |
| 0.0001 | 23.57 | 7000 | 0.4614 | 0.6469 |
| 0.0001 | 26.94 | 8000 | 0.4657 | 0.6464 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.1.dev0
- Tokenizers 0.13.3
|