File size: 3,490 Bytes
a38e05f 4ec307d a38e05f 4ec307d a38e05f 4ec307d 748391b 7ed9984 243cd15 a38e05f 243cd15 a38e05f 7ed9984 a38e05f |
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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
---
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"]
```
|