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---
language:
- zh
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Distil-Whisper Small zh-HK - Alvin
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_0 yue
type: mozilla-foundation/common_voice_16_0
config: yue
split: test
args: yue
metrics:
- name: Normalized CER
type: cer
value: 9.77
---
<!-- 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. -->
# Distil-Whisper Small zh-HK - Alvin
This model is a distilled and fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Cantonese language. It achieves a 9.77 CER (without punctuations), 11.7 CER (with punctuations) on Common Voice 16.0
## Training and evaluation data
For training,
- CantoMap: Winterstein, Grégoire, Tang, Carmen and Lai, Regine (2020) "CantoMap: a Hong Kong Cantonese MapTask Corpus", in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille: European Language Resources Association, p. 2899-2906.
- Cantonse-ASR: Yu, Tiezheng, Frieske, Rita, Xu, Peng, Cahyawijaya, Samuel, Yiu, Cheuk Tung, Lovenia, Holy, Dai, Wenliang, Barezi, Elham, Chen, Qifeng, Ma, Xiaojuan, Shi, Bertram, Fung, Pascale (2022) "Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset", 2022. Link: https://arxiv.org/pdf/2201.02419.pdf
- Common Voice yue and zh-HK train sets
For evaluation, Common Voice 16.0 yue Test set is used.
## Results
- CER (lower is better): 0.117
- GPU Inference with Fast Attention (example below): 0.039s/sample
- Note all GPU evaluations are done on RTX 3090 GPU
- GPU Inference: <TODO>s/sample
- CPU Inference: 2.57s/sample
- GPU VRAM: ~2 GB
## Using the Model
```
import librosa
import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor
y, sr = librosa.load('audio.mp3', sr=16000)
MODEL_NAME = "alvanlii/distil-whisper-small-cantonese"
processor = WhisperProcessor.from_pretrained(MODEL_NAME)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME)
model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
model.config.use_cache = False
processed_in = processor(y, sampling_rate=sr, return_tensors="pt")
gout = model.generate(
input_features=processed_in.input_features,
output_scores=True, return_dict_in_generate=True
)
transcription = processor.batch_decode(gout.sequences, skip_special_tokens=True)[0]
print(transcription)
```
- Alternatively, you can use huggingface pipelines
```
from transformers import pipeline
MODEL_NAME = "alvanlii/distil-whisper-small-cantonese"
lang = "zh"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
text = pipe(file)["text"]
```
## Model Speedup
Just add attn_implementation="sdpa" for Flash Attention.
```
model = AutoModelForSpeechSeq2Seq.from_pretrained(
"alvanlii/distil-whisper-small-cantonese",
torch_dtype=torch_dtype,
low_cpu_mem_usage=True,
use_safetensors=True,
attn_implementation="sdpa",
)
```
Using Flash Attention reduced the amount of time taken per sample from <TODO>s to 0.039s.
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