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