File size: 1,720 Bytes
4081c29 155127c 4081c29 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
language:
- km
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- openslr
- google/fleurs
- seanghay/km-speech-corpus
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: test
metrics:
- name: Wer
type: wer
value: 0.6165
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
<!-- 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-v2
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: 0.26
- Wer: 0.6165
## 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
``` |