File size: 795 Bytes
d697a3c 0a92ca3 d697a3c c3dbb4c 0a92ca3 c3dbb4c 0a92ca3 d697a3c 0a92ca3 d697a3c 0a92ca3 d697a3c 0a92ca3 d697a3c 0a92ca3 d697a3c 0a92ca3 c3dbb4c 0a92ca3 d697a3c |
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 |
import os
os.system("pip install git+https://github.com/openai/whisper.git")
import gradio as gr
import whisper
model = whisper.load_model("large")
def inference(audio):
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(model.device)
_, probs = model.detect_language(mel)
options = whisper.DecodingOptions(fp16 = False)
result = whisper.decode(model, mel, options)
print(result.text)
return result.text, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
iface = gr.Interface(
fn=inference,
inputs=gr.Audio(type="filepath", label="上传音频文件 (.mp3, .wav等)"),
outputs="text"
)
# 启动 Gradio 界面
iface.launch()
|