initial commit

.devcontainer/devcontainer.json

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+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-dockerfile
+{
+	"name": "Existing Dockerfile",
+	"build": {
+		// Sets the run context to one level up instead of the .devcontainer folder.
+		"context": "..",
+		// Update the 'dockerFile' property if you aren't using the standard 'Dockerfile' filename.
+		"dockerfile": "../Dockerfile"
+	}
+
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+
+	// Uncomment the next line to run commands after the container is created.
+	// "postCreateCommand": "cat /etc/os-release",
+
+	// Configure tool-specific properties.
+	// "customizations": {},
+
+	// Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "devcontainer"
+}

.gitignore

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+# 忽略 Python 虛擬環境
+venv/
+
+# 忽略 VS Code 的設定
+.vscode/
+
+# 忽略 Python 的快取檔案
+__pycache__/
+*.pyc
+
+# 忽略下載的本地模型 (非常重要，因為它太大了！)
+ASRs/
+
+# 忽略音訊檔案 (如果它們只是測試用的話)
+TestAudio/
+*.wav

ASR.py

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+import torch
+import soundfile as sf
+import librosa
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import os
+from phonemizer import phonemize
+import numpy as np
+
+# --- 1, 2, 3, 4 部分與之前版本完全相同，此處省略以保持簡潔 ---
+# ...
+# --- 1. 全域設定 ---
+TARGET_SENTENCE = "how was your day"
+AUDIO_FILE_PATH = "./TestAudio/hello.wav"
+MODEL_NAME = "MultiBridge/wav2vec-LnNor-IPA-ft"
+MODEL_SAVE_PATH = "./ASRs/MultiBridge-wav2vec-LnNor-IPA-ft-local"
+
+# --- 2. 載入模型和處理器 ---
+print(f"正在準備模型 '{MODEL_NAME}'...")
+try:
+    if not os.path.exists(MODEL_SAVE_PATH):
+        print(f"本地找不到模型，正在從 Hugging Face 下載並儲存...")
+        processor_to_save = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
+        model_to_save = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)
+        processor_to_save.save_pretrained(MODEL_SAVE_PATH)
+        model_to_save.save_pretrained(MODEL_SAVE_PATH)
+        print("模型已成功下載並儲存。")
+    else:
+        print(f"在 '{MODEL_SAVE_PATH}' 中找到本地模型。")
+    processor = Wav2Vec2Processor.from_pretrained(MODEL_SAVE_PATH)
+    model = Wav2Vec2ForCTC.from_pretrained(MODEL_SAVE_PATH)
+    print("模型和處理器載入成功！")
+except Exception as e:
+    print(f"處理或載入模型時發生錯誤: {e}")
+    exit()
+
+# --- 3. 準備目標音標 (Target) ---
+print("正在準備目標音標...")
+target_ipa_by_word = phonemize(
+    TARGET_SENTENCE, language='en-us', backend='espeak', with_stress=True
+).split()
+
+# --- 4. 讀取音訊並進行簡單辨識 ---
+print(f"正在讀取音訊檔案: {AUDIO_FILE_PATH}...")
+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:
+    print(f"讀取或處理音訊時發生錯誤: {e}")
+    exit()
+print("正在辨識用戶的實際發音...")
+input_values = processor(speech, sampling_rate=16000, return_tensors="pt").input_values
+with torch.no_grad():
+  logits = model(input_values).logits
+predicted_ids = torch.argmax(logits, dim=-1)
+user_ipa_full = processor.decode(predicted_ids[0])
+
+
+# --- 5. 核心函式：返回按單詞分割的詳細對齊路徑 (與之前版本相同) ---
+def get_phoneme_alignments_by_word(user_phoneme_str, target_words_ipa):
+    user_phonemes = list(user_phoneme_str.replace(' ', ''))
+    target_phonemes_flat = []
+    word_boundaries = []
+    current_idx = 0
+    for word_ipa in target_words_ipa:
+        phonemes = list(word_ipa.replace('ˌ', '').replace('ˈ', ''))
+        target_phonemes_flat.extend(phonemes)
+        current_idx += len(phonemes)
+        word_boundaries.append(current_idx)
+
+    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 = []
+    user_word_start_idx = 0
+    target_phoneme_count = 0
+    
+    for i, phoneme in enumerate(target_path):
+        if phoneme != '-':
+            target_phoneme_count += 1
+        
+        if target_phoneme_count in word_boundaries:
+            target_alignment = target_path[user_word_start_idx:i+1]
+            user_alignment = user_path[user_word_start_idx:i+1]
+            alignments_by_word.append({
+                "target": target_alignment,
+                "user": user_alignment
+            })
+            user_word_start_idx = i + 1
+            
+    return alignments_by_word
+
+# --- 6. 最終的、格式完美的輸出函式 ---
+def format_and_print_final_version(alignments):
+    target_line_parts = []
+    user_line_parts = []
+
+    for alignment in alignments:
+        # 為每個單詞的對齊計算最大寬度
+        max_lens = [max(len(t), len(u)) for t, u in zip(alignment['target'], alignment['user'])]
+        
+        # 格式化 Target 部分
+        target_word_parts = [phoneme.ljust(max_lens[i]) for i, phoneme in enumerate(alignment['target'])]
+        target_line_parts.append(f"[ {' '.join(target_word_parts)} ]")
+        
+        # 格式化 User 部分
+        user_word_parts = [phoneme.ljust(max_lens[i]) for i, phoneme in enumerate(alignment['user'])]
+        user_line_parts.append(f"[ {' '.join(user_word_parts)} ]")
+
+    # 組合並列印最終結果
+    print(f"Target  : {' '.join(target_line_parts)}")
+    print(f"User    : {' '.join(user_line_parts)}")
+
+
+# --- 主流程 ---
+print("正在進行音素級對齊...")
+word_alignments = get_phoneme_alignments_by_word(user_ipa_full, target_ipa_by_word)
+
+print("\n" + "="*60)
+print("                 發音對比分析結果")
+print("="*60)
+print(f"Sentence: {TARGET_SENTENCE}\n")
+format_and_print_final_version(word_alignments)
+print("="*60)

Dockerfile

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+# 1. 選擇一個包含 Python 的官方 Linux 映像
+FROM python:3.10-slim
+
+# 2. 設定容器內的工作目錄
+WORKDIR /app
+
+# 3. 安裝系統級依賴 (最關鍵的一步：安裝 espeak-ng 和其他工具)
+#    -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 \
+    espeak-ng \
+    libsndfile1 \
+    ffmpeg \
+    wget && \
+    rm -rf /var/lib/apt/lists/*
+
+# 4. 複製 requirements.txt 檔案到容器中並安裝 Python 套件
+COPY requirements.txt .
+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
+COPY . .
+
+# 這行是可選的，它設定了當容器直接執行時的預設命令
+# CMD ["python", "your_script.py"]

requirements.txt

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+torch
+soundfile
+librosa
+transformers
+phonemizer