changed dockerfile

.devcontainer/devcontainer.json

@@ -1,6 +1,11 @@
 {
     "name": "FYP Backend (GPU)",
-    "image": "e226274b3239", // 直接使用您已有的鏡像 ID
+
+    "build": {
+        // 假設您的 Dockerfile 位於專案根目錄
+        "dockerfile": "../Dockerfile", 
+        "context": ".."
+    },
 
     // 這是最最最關鍵的部分！
     "runArgs": [
@@ -18,7 +23,7 @@
     "shutdownAction": "none",
 
     // 在容器創建後運行的命令 (可選，但推薦)
-    "postCreateCommand": "pip install -r requirements.txt",
+    //"postCreateCommand": "pip install -r requirements.txt",
 
     // VS Code 擴展推薦 (可選)
     "customizations": {

Dockerfile

@@ -11,6 +11,11 @@ WORKDIR /app
 #    -y 自動回答 'yes'
 #    --no-install-recommends 避免安裝不必要的建議套件，保持映像檔小巧
 RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    cmake \
+    mecab \
+    libmecab-dev \
+    mecab-ipadic-utf8 \
     espeak-ng \
     libsndfile1 \
     ffmpeg \
@@ -20,6 +25,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 
 # 4. 複製 requirements.txt 檔案到容器中並安裝 Python 套件
 COPY requirements.txt .
+RUN pip install --upgrade pip
 RUN pip install --no-cache-dir -r requirements.txt
 
 # 5. 將專案中的所有其他檔案複製到容器中

analyzer/ASR_jp_jp.py

@@ -0,0 +1,290 @@
+# =======================================================================
+# 1. 匯入區 (Imports)
+#    【關鍵修改】新增了 pyopenjtalk 和 MeCab 的匯入
+# =======================================================================
+import torch
+import soundfile as sf
+import librosa
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import os
+import pyopenjtalk
+import MeCab
+import numpy as np
+from datetime import datetime, timezone
+import re
+
+# =======================================================================
+# 2. 全域變數與配置區 (Global Variables & Config)
+# =======================================================================
+# 【關鍵修改】自動檢測可用設備
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"INFO: ASR_jp_jp.py is configured to use device: {DEVICE}")
+
+# 【關鍵修改】設定為日語 ASR 模型
+MODEL_NAME = "prj-beatrice/japanese-hubert-base-phoneme-ctc-v3"
+
+processor = None
+model = None
+
+# 【關鍵修改】初始化 MeCab 分詞器
+# 我們使用 -Owakati 選項來獲得以空格分隔的單詞列表
+mecab_tagger = MeCab.Tagger("-Owakati")
+
+# =======================================================================
+# 3. 核心業務邏輯區 (Core Business Logic)
+# =======================================================================
+
+# -----------------------------------------------------------------------
+# 3.1. 模型載入函數 (與其他版本邏輯相同)
+# -----------------------------------------------------------------------
+def load_model():
+    """
+    載入日語 ASR 模型和對應的處理器。
+    """
+    global processor, model
+    if processor and model:
+        print(f"模型 '{MODEL_NAME}' 已載入，跳過。")
+        return True
+
+    print(f"正在準備 ASR 模型 '{MODEL_NAME}'...")
+    try:
+        processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
+        model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)
+        model.to(DEVICE)
+        print(f"模型 '{MODEL_NAME}' 和處理器載入成功！")
+        return True
+    except Exception as e:
+        print(f"處理或載入模型 '{MODEL_NAME}' 時發生錯誤: {e}")
+        raise RuntimeError(f"Failed to load model '{MODEL_NAME}': {e}")
+
+# -----------------------------------------------------------------------
+# 3.2. 日語 G2P 輔助函數 (這是此檔案最核心的新增部分)
+# -----------------------------------------------------------------------
+def japanese_g2p(text: str) -> list[tuple[str, str]]:
+    """
+    將日語句子轉換為 (單詞, 對應音素) 的元組列表。
+    這是我們為日語定製的 G2P 核心。
+    """
+    # 1. 使用 MeCab 進行分詞
+    words = mecab_tagger.parse(text).strip().split(' ')
+    
+    # 2. 對整個句子使用 PyOpenJTalk 獲取完整的音素序列
+    #    我們直接使用 pyopenjtalk.g2p，它輸出的就是以空格分隔的音素
+    full_phonemes_str = pyopenjtalk.g2p(text)
+    
+    # 3. 進行音素清理，以匹配 ASR 模型的輸出
+    #    ASR 模型輸出的是清音，所以我們移除濁音、半濁音、長音等符號
+    cleaned_phonemes = full_phonemes_str.replace('pau', ' ').replace(' ', '').replace('N', 'n').replace('cl', '')
+    
+    # 4. 將單詞和音素進行配對
+    #    這是一個簡化的配對邏輯：我們假設音素的數量和假名的數量大致對應
+    #    這在大多數情況下是有效的，因為日語是音節語言
+    result = []
+    phoneme_idx = 0
+    for word in words:
+        # 計算當前單詞大致對應多少個音素 (假名數量)
+        num_mora = len(word)
+        
+        # 提取對應的音素片段
+        word_phonemes = cleaned_phonemes[phoneme_idx : phoneme_idx + num_mora]
+        
+        # 檢查提取的音素是否為空，避免無效單詞的影響
+        if word_phonemes:
+            result.append((word, word_phonemes))
+        
+        phoneme_idx += num_mora
+        
+    return result
+
+# -----------------------------------------------------------------------
+# 3.3. 音素切分函數 (與其他版本邏輯相同，但更通用)
+# -----------------------------------------------------------------------
+def _tokenize_ipa(ipa_string: str) -> list:
+    """
+    將音素字串切分為列表。對於日語，直接按字元切分即可。
+    """
+    # 日語 ASR 模型的輸出是單字元音素，所以直接轉換為列表
+    return list(ipa_string)
+
+# -----------------------------------------------------------------------
+# 3.4. 核心分析函數 (主入口，已修改為日語邏輯)
+# -----------------------------------------------------------------------
+def analyze(audio_file_path: str, target_sentence: str) -> dict:
+    """
+    接收音訊檔案路徑和目標日語句子，回傳詳細的發音分析字典。
+    """
+    if not processor or not model:
+        raise RuntimeError("模型尚未載入。請確保在呼叫 analyze 之前已成功執行 load_model()。")
+
+    # 【關鍵修改】使用我們新的日語 G2P 函數
+    g2p_result = japanese_g2p(target_sentence)
+    
+    # 從 G2P 結果中提取原始單詞列表和按單詞劃分的音素列表
+    target_words_original = [item[0] for item in g2p_result]
+    target_ipa_by_word = [_tokenize_ipa(item[1]) for item in g2p_result]
+
+    # 載入並處理音訊 (與其他版本邏輯相同)
+    try:
+        speech, sample_rate = sf.read(audio_file_path)
+        if sample_rate != 16000:
+            speech = librosa.resample(y=speech, orig_sr=sample_rate, target_sr=16000)
+    except Exception as e:
+        raise IOError(f"讀取或處理音訊時發生錯誤: {e}")
+    
+    # 進行 ASR 推論 (與其他版本邏輯相同)
+    input_values = processor(speech, sampling_rate=16000, return_tensors="pt").input_values
+    input_values = input_values.to(DEVICE)
+    with torch.no_grad():
+        logits = model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    user_ipa_full = processor.decode(predicted_ids[0])
+
+    # 進行對齊 (與其他版本邏輯相同)
+    word_alignments = _get_phoneme_alignments_by_word(user_ipa_full, target_ipa_by_word)
+
+    # 格式化輸出 (與其他版本邏輯相同)
+    return _format_to_json_structure(word_alignments, target_sentence, target_words_original)
+
+# =======================================================================
+# 4. 對齊與格式化函數區 (Alignment & Formatting)
+#    【注意】這些函數是語言無關的，直接從英文版複製，無需修改
+# =======================================================================
+
+# -----------------------------------------------------------------------
+# 4.1. 對齊函數
+# -----------------------------------------------------------------------
+def _get_phoneme_alignments_by_word(user_phoneme_str, target_words_ipa_tokenized):
+    """
+    執行音素對齊。此函數是語言無關的。
+    """
+    user_phonemes = _tokenize_ipa(user_phoneme_str)
+    
+    target_phonemes_flat = []
+    word_boundaries_indices = [] 
+    current_idx = 0
+    for word_ipa_tokens in target_words_ipa_tokenized:
+        target_phonemes_flat.extend(word_ipa_tokens)
+        current_idx += len(word_ipa_tokens)
+        word_boundaries_indices.append(current_idx - 1)
+
+    dp = np.zeros((len(user_phonemes) + 1, len(target_phonemes_flat) + 1))
+    for i in range(1, len(user_phonemes) + 1): dp[i][0] = i
+    for j in range(1, len(target_phonemes_flat) + 1): dp[0][j] = j
+    for i in range(1, len(user_phonemes) + 1):
+        for j in range(1, len(target_phonemes_flat) + 1):
+            cost = 0 if user_phonemes[i-1] == target_phonemes_flat[j-1] else 1
+            dp[i][j] = min(dp[i-1][j] + 1, dp[i][j-1] + 1, dp[i-1][j-1] + cost)
+
+    i, j = len(user_phonemes), len(target_phonemes_flat)
+    user_path, target_path = [], []
+    while i > 0 or j > 0:
+        cost = float('inf') if i == 0 or j == 0 else (0 if user_phonemes[i-1] == target_phonemes_flat[j-1] else 1)
+        if i > 0 and j > 0 and dp[i][j] == dp[i-1][j-1] + cost:
+            user_path.insert(0, user_phonemes[i-1]); target_path.insert(0, target_phonemes_flat[j-1]); i -= 1; j -= 1
+        elif i > 0 and dp[i][j] == dp[i-1][j] + 1:
+            user_path.insert(0, user_phonemes[i-1]); target_path.insert(0, '-'); i -= 1
+        else:
+            user_path.insert(0, '-'); target_path.insert(0, target_phonemes_flat[j-1]); j -= 1
+    
+    alignments_by_word = []
+    word_start_idx_in_path = 0
+    target_phoneme_counter_in_path = 0
+
+    for path_idx, p in enumerate(target_path):
+        if p != '-':
+            if target_phoneme_counter_in_path in word_boundaries_indices:
+                target_alignment = target_path[word_start_idx_in_path : path_idx + 1]
+                user_alignment = user_path[word_start_idx_in_path : path_idx + 1]
+                
+                alignments_by_word.append({
+                    "target": target_alignment,
+                    "user": user_alignment
+                })
+                
+                word_start_idx_in_path = path_idx + 1
+            
+            target_phoneme_counter_in_path += 1
+            
+    return alignments_by_word
+
+# -----------------------------------------------------------------------
+# 4.2. 格式化函數
+# -----------------------------------------------------------------------
+def _format_to_json_structure(alignments, sentence, original_words) -> dict:
+    """
+    將對齊結果格式化為最終的 JSON 結構。此函數是語言無關的。
+    """
+    total_phonemes = 0
+    total_errors = 0
+    correct_words_count = 0
+    words_data = []
+
+    num_words_to_process = min(len(alignments), len(original_words))
+
+    for i in range(num_words_to_process):
+        alignment = alignments[i]
+        word_is_correct = True
+        phonemes_data = []
+        
+        for j in range(len(alignment['target'])):
+            target_phoneme = alignment['target'][j]
+            user_phoneme = alignment['user'][j]
+            is_match = (user_phoneme == target_phoneme)
+            
+            phonemes_data.append({
+                "target": target_phoneme,
+                "user": user_phoneme,
+                "isMatch": is_match
+            })
+            
+            if not is_match:
+                word_is_correct = False
+                if not (user_phoneme == '-' and target_phoneme == '-'):
+                    total_errors += 1
+        
+        if word_is_correct:
+            correct_words_count += 1
+            
+        words_data.append({
+            "word": original_words[i],
+            "isCorrect": word_is_correct,
+            "phonemes": phonemes_data
+        })
+        
+        total_phonemes += sum(1 for p in alignment['target'] if p != '-')
+
+    total_words = len(original_words)
+    if len(alignments) < total_words:
+        for i in range(len(alignments), total_words):
+            # 處理使用者未說出的單詞
+            missed_word_ipa = _tokenize_ipa(japanese_g2p(original_words[i])[0][1]) # 重新獲取音素
+            phonemes_data = []
+            for p_ipa in missed_word_ipa:
+                phonemes_data.append({"target": p_ipa, "user": "-", "isMatch": False})
+                total_errors += 1
+                total_phonemes += 1
+
+            words_data.append({
+                "word": original_words[i],
+                "isCorrect": False,
+                "phonemes": phonemes_data
+            })
+
+    overall_score = (correct_words_count / total_words) * 100 if total_words > 0 else 0
+    phoneme_error_rate = (total_errors / total_phonemes) * 100 if total_phonemes > 0 else 0
+
+    final_result = {
+        "sentence": sentence,
+        "analysisTimestampUTC": datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S (UTC)'),
+        "summary": {
+            "overallScore": round(overall_score, 1),
+            "totalWords": total_words,
+            "correctWords": correct_words_count,
+            "phonemeErrorRate": round(phoneme_error_rate, 2),
+            "total_errors": total_errors,
+            "total_target_phonemes": total_phonemes
+        },
+        "words": words_data
+    }
+    
+    return final_result

requirements.txt

@@ -9,4 +9,6 @@ transformers
 phonemizer[espeak]
 numpy
 epitran
-g2p
+g2p
+pyopenjtalk
+mecab-python3