app.py

import os
import time
import json
import gradio as gr
import torch
import torchaudio
import numpy as np
from denoiser.demucs import Demucs
from pydub import AudioSegment

modelpath = './denoiser/master64.th'

def transcribe(file_upload, microphone):
    file = microphone if microphone is not None else file_upload
    
    # 載入模型
    model = Demucs(hidden=64)
    state_dict = torch.load(modelpath, map_location='cpu')
    model.load_state_dict(state_dict)
    
    # 載入音訊並強制轉單聲道
    x, sr = torchaudio.load(file, channels_first=True)  # 確保通道優先格式
    if x.shape[0] > 1:
        x = torch.mean(x, dim=0, keepdim=True)  # 平均所有通道轉單聲道
    
    # 執行降噪
    out = model(x[None])[0]  # 增加batch維度
    
    # 後處理
    out = out / max(out.abs().max().item(), 1)
    torchaudio.save('enhanced.wav', out, sr)
    
    # 降低位元率（僅供語音辨識使用）
    enhanced = AudioSegment.from_wav('enhanced.wav')
    enhanced.export('enhanced.wav', format="wav", bitrate="256k")
    
    return "enhanced.wav"

# import os
# import time
# import json
# import gradio as gr
# import torch
# import torchaudio
# import numpy as np
# from denoiser.demucs import Demucs
# from pydub import AudioSegment
# import soundfile as sf
# import librosa

# modelpath = './denoiser/master64.th'

# def transcribe(file_upload, microphone):
#     file = microphone if microphone is not None else file_upload

#     # 新增音訊預處理 → 統一格式
#     def preprocess_audio(path):
#         data, sr = sf.read(path)
        
#         # 如果是雙聲道 → 轉單聲道
#         if len(data.shape) > 1:
#             data = data.mean(axis=1)

#         # 如果不是 16kHz → 重採樣
#         if sr != 16000:
#             data = librosa.resample(data, orig_sr=sr, target_sr=16000)
#             sr = 16000

#         # 儲存為 WAV 供模型使用
#         sf.write("enhanced.wav", data, sr)
#         return "enhanced.wav"

#     # 如果是 MP3，先轉成 WAV 再處理
#     if file.lower().endswith(".mp3"):
#         audio = AudioSegment.from_file(file)
#         audio = audio.set_frame_rate(16000).set_channels(1)  # 轉單聲道 + 16kHz
#         audio.export("enhanced.wav", format="wav")
#         file = "enhanced.wav"
#     else:
#         file = preprocess_audio(file)

#     model = Demucs(hidden=64)
#     state_dict = torch.load(modelpath, map_location='cpu')
#     model.load_state_dict(state_dict)
#     demucs = model.eval()

#     x, sr = torchaudio.load(file)
#     x = x[0:1]  # 強制取第一個聲道（確保是單聲道）

#     with torch.no_grad():
#         out = demucs(x[None])[0]

#     out = out / max(out.abs().max().item(), 1)
#     torchaudio.save('enhanced_final.wav', out, sr)

#     # 輸出 WAV 格式給前端播放
#     enhanced = AudioSegment.from_wav('enhanced_final.wav')
#     enhanced.export('enhanced_final.mp3', format="mp3", bitrate="256k")

#     return "enhanced_final.mp3"  # 回傳 MP3 更省空間


# # 👇 加上這一行，解決 Gradio schema 推導錯誤
# transcribe.__annotations__ = {
#     "file_upload": str,
#     "microphone": str,
#     "return": str
# }

demo = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(type="filepath", label="語音質檢原始音檔", sources=["upload", "microphone"])  # 顯式指定來源
    ],
    outputs=[
        gr.Audio(type="filepath", label="Output")  # 保持列表形式
    ],
    title="<h1>語音質檢/噪音去除 (語音增強)</h1>",
    description="""<h2><a href='https://www.twman.org' target='_blank'>TonTon Huang Ph.D.</a> | <a href='https://blog.twman.org/p/deeplearning101.html' target='_blank'>手把手帶你一起踩AI坑</a><br></h2><br>
                為了提升語音識別的效果，可以在識別前先進行噪音去除<br>
                    <a href='https://github.com/Deep-Learning-101' target='_blank'>Deep Learning 101 Github</a> | <a href='http://deeplearning101.twman.org' target='_blank'>Deep Learning 101</a> | <a href='https://www.facebook.com/groups/525579498272187/' target='_blank'>台灣人工智慧社團 FB</a> | <a href='https://www.youtube.com/c/DeepLearning101' target='_blank'>YouTube</a><br>
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                    <a href='https://blog.twman.org/2023/04/GPT.html' target='_blank'>Large Language Model，LLM</a>：探討 LLM 的發展與應用，強調硬體資源在開發中的關鍵作用。。<br>
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                    <a href='https://blog.twman.org/2021/04/NLP.html' target='_blank'>那些自然語言處理 (Natural Language Processing, NLP) 踩的坑</a>：分享 NLP 領域的實踐經驗，強調數據質量對模型效果的影響。<br>
                    <a href='https://blog.twman.org/2021/04/ASR.html' target='_blank'>那些語音處理 (Speech Processing) 踩的坑</a>：分享語音處理領域的實務經驗，強調資料品質對模型效果的影響。<br>
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                <a href='https://github.com/facebookresearch/denoiser' target='_blank'> Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)</a>""",
    allow_flagging="never",
    # examples=[
    #     ["exampleAudio/15s_2020-03-27_sep1.wav"],
    #     ["exampleAudio/13s_2020-03-27_sep2.wav"],
    # ],
)

demo.launch(debug=True, share=True)