app.py

import gradio as gr
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import librosa

# 初始化组件
def load_components():
    processor = WhisperProcessor.from_pretrained(
        "openai/whisper-large-v3-turbo", 
        language="Japanese",
        task="transcribe"
    )
    
    model = WhisperForConditionalGeneration.from_pretrained(
        "AkitoP/whisper-jsut5000-voicevox-phone-lora", 
        device_map="auto" if torch.cuda.is_available() else "cpu"
    )
    
    # 设置强制解码参数
    forced_decoder_ids = processor.get_decoder_prompt_ids(
        language="japanese", 
        task="transcribe"
    )
    model.config.forced_decoder_ids = forced_decoder_ids
    model.eval()
    
    return processor, model

processor, model = load_components()

# 语音转文字函数
def transcribe_audio(audio_path):
    try:
        # 加载音频
        audio, sr = librosa.load(audio_path, sr=16000)
        
        # 提取特征
        inputs = processor.feature_extractor(
            audio,
            sampling_rate=16000,
            return_tensors="pt"
        ).input_features.to(model.device)
        
        # 生成预测
        with torch.no_grad():
            generated_ids = model.generate(inputs, max_length=256)
        
        # 解码结果
        text = processor.tokenizer.batch_decode(
            generated_ids, 
            skip_special_tokens=True
        )[0]
        
        return text
    
    except Exception as e:
        return f"Error: {str(e)}"

# 创建Gradio界面
demo = gr.Interface(
    fn=transcribe_audio,
    inputs=gr.Audio(sources="upload", type="filepath"),
    outputs=gr.Textbox(label="output"),
    title="ASR",
    description="Whisper trained on Prosodic&Phonemic transcription on JSUT5000 & VOICEVOX generated dataset.",
    allow_flagging="never"
)

demo.launch()