Lite Whisper Large V3

🚀 Lite-Whisper large-v3

Lite-Whisper是使用LiteASR對OpenAI Whisper進行壓縮後的版本。如需瞭解詳細信息，請查看我們的GitHub倉庫和論文。

🚀 快速開始

以下是一個快速上手的代碼片段：

import librosa 
import torch
from transformers import AutoProcessor, AutoModel

device = "cuda:0"
dtype = torch.float16

# load the compressed Whisper model
model = AutoModel.from_pretrained(
    "efficient-speech/lite-whisper-large-v3-turbo", 
    trust_remote_code=True, 
)
model.to(dtype).to(device)

# we use the same processor as the original model
processor = AutoProcessor.from_pretrained("openai/whisper-large-v3")

# set the path to your audio file
path = "path/to/audio.wav"
audio, _ = librosa.load(path, sr=16000)

input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
input_features = input_features.to(dtype).to(device)

predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(
    predicted_ids, 
    skip_special_tokens=True
)[0]

print(transcription)

📊 基準測試結果

以下是在ESB數據集上評估得到的平均單詞錯誤率（WER）：

📄 許可證

本項目採用Apache 2.0許可證。