whisper-small-khmer

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whisper-small-khmer

This model is a fine-tuned version of 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
• model.onnx

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

ffmpeg -i audio.ogg -ar 16000 -ac 1 -c:a pcm_s16le output.wav

2. Transcribe with whisper.cpp

./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 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

Framework versions

• Transformers 4.28.0.dev0
• Pytorch 2.0.0+cu117
• Datasets 2.11.1.dev0
• Tokenizers 0.13.3