|
--- |
|
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: 9.7 |
|
--- |
|
<!-- 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 version of [alvanlii/whisper-small-cantonese](https://huggingface.co/alvanlii/whisper-small-cantonese) on the Cantonese language. |
|
- Achieves a 9.7 CER (without punctuations), 11.59 CER (with punctuations) on Common Voice 16.0. |
|
- Has 3 decoder layers instead of regular 12 of the Whisper small model. |
|
- Uses ~2GB of GPU VRAM |
|
|
|
## 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. |
|
|
|
## Comparisons to Whisper Small |
|
||`alvanlii/distil-whisper-small-cantonese`|`alvanlii/whisper-small-cantonese`| |
|
|--|--|--| |
|
|CER (lower is better)|0.097|0.089| |
|
|GPU Inference time (sdpa) [s/sample]|0.027|0.055| |
|
|GPU Inference (regular) [s/sample]|0.027|0.308| |
|
|CPU Inference [s/sample]|1.3|2.57| |
|
|Params [M]|157|242| |
|
|
|
Note: inference time is calculated by taking the average inference time for the CV16 yue test set |
|
|
|
## 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"] |
|
``` |
|
|
