metadata
license: apache-2.0
metrics:
- cer
Welcome
If you find this model helpful, please like this model and star us on https://github.com/LianjiaTech/BELLE !
Belle-distilwhisper-large-v2-zh
Fine tune distilwhisper-large-v2 to enhance Chinese speech recognition capabilities.
Similar to distilwhisper-large-v2, Belle-distilwhisper-large-v2-zh is 5.8 times faster and has 51% fewer parameters compared to whisper-large-v2.
Despite having 51% fewer parameters, Belle-distilwhisper-large-v2-zh achieves a relative improvement of -3% to 35% over whisper-large-v2.
It's important to note that the original distilwhisper-large-v2 cannot transcribe Chinese (it only outputs English).
Usage
from transformers import pipeline
transcriber = pipeline(
"automatic-speech-recognition",
model="BELLE-2/Belle-distilwhisper-large-v2-zh"
)
transcriber.model.config.forced_decoder_ids = (
transcriber.tokenizer.get_decoder_prompt_ids(
language="zh",
task="transcribe"
)
)
transcription = transcriber("my_audio.wav")
Fine-tuning
| Model | (Re)Sample Rate | Train Datasets | Fine-tuning (full or peft) |
|---|---|---|---|
| Belle-distilwhisper-large-v2-zh | 16KHz | AISHELL-1 AISHELL-2 WenetSpeech HKUST | full fine-tuning |
If you want to fine-thuning the model on your datasets, please reference to the github repo
CER(%) ↓
| Model | Parameters(M) | Language Tag | aishell_1_test( ↓ ) | aishell_2_test( ↓ ) | wenetspeech_net ( ↓ ) | wenetspeech_meeting( ↓ ) | HKUST_dev( ↓ ) |
|---|---|---|---|---|---|---|---|
| whisper-large-v2 | 1550 | Chinese | 8.818% | 6.183% | 12.343% | 26.413% | 31.917% |
| distilwhisper-large-v2 | 756 | Chinese | - | - | - | - | - |
| Belle-distilwhisper-large-v2-zh | 756 | Chinese | 5.958% | 6.477% | 12.786% | 17.039% | 20.771% |
Citation
Please cite our paper and github when using our code, data or model.
@misc{BELLE,
author = {BELLEGroup},
title = {BELLE: Be Everyone's Large Language model Engine},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
}