added french version

analyzer/ASR_fr_fr.py

@@ -0,0 +1,251 @@
+import torch
+import soundfile as sf
+import librosa
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import os
+from phonemizer import phonemize
+import numpy as np
+from datetime import datetime, timezone
+import unicodedata
+import re
+import epitran
+
+# --- 1. 全域設定與模型載入函數 (已修改為法語模型) ---
+MODEL_NAME = "Cnam-LMSSC/wav2vec2-french-phonemizer"
+MODEL_SAVE_PATH = "./ASRs/Cnam-LMSSC-wav2vec2-french-phonemizer-local"
+
+processor = None
+model = None
+
+def load_model():
+    """
+    在應用程式啟動時載入法語模型和處理器。
+    如果模型已載入，則跳過。
+    """
+    global processor, model
+    if processor and model:
+        print("法語模型已載入，跳過。")
+        return True
+
+    print(f"正在準備法語 (fr-fr) ASR 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("法語 (fr-fr) 模型和處理器載入成功！")
+        return True
+    except Exception as e:
+        print(f"處理或載入 fr-fr 模型時發生錯誤: {e}")
+        raise RuntimeError(f"Failed to load fr-fr model: {e}")
+
+def _tokenize_unicode_ipa(ipa_string: str) -> list:
+    """
+    智能地切分包含 Unicode 組合字元的 IPA 字串。
+    """
+    phonemes = []
+    # 移除所有空格
+    s = ipa_string.replace(' ', '')
+    
+    i = 0
+    while i < len(s):
+        # 獲取當前字元
+        current_char = s[i]
+        i += 1
+        # 檢查後續是否有連續的組合字元
+        while i < len(s) and unicodedata.category(s[i]) == 'Mn': # 'Mn' 代表非間距標記 (Non-Spacing Mark)
+            current_char += s[i]
+            i += 1
+        phonemes.append(current_char)
+    return phonemes
+
+# --- 2. 核心分析函數 (主入口) (已修改為法語邏輯) ---
+def analyze(audio_file_path: str, target_sentence: str) -> dict:
+    """
+    接收音訊檔案路徑和目標句子，回傳詳細的發音分析字典。
+    這是此模組的主要進入點。
+    """
+    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 1：更智能地處理原始句子 】】】】】
+    # 使用正則表達式來準確地分割單詞，並自動忽略標點符號
+    target_words_original = re.findall(r"[\w'-]+", target_sentence)
+    # 將分割好的、乾淨的單詞重新組合，再傳給 phonemize  
+    cleaned_sentence = " ".join(target_words_original)
+
+    # 使用 espeak 獲取法語目標音素
+    epi_fr = epitran.Epitran('fra-Latn')
+    target_ipa_full = epi_fr.transliterate(cleaned_sentence)
+    target_ipa_by_word_str = target_ipa_full.split()
+
+    # 【【【【【 確保兩個列表長度一致 】】】】】
+    if len(target_ipa_by_word_str) != len(target_words_original):
+        target_words_original = target_words_original[:len(target_ipa_by_word_str)]
+
+    # 對於法語，我們將特殊符號移除，並使用簡單的字元切分
+    target_ipa_by_word = [
+        _tokenize_unicode_ipa(word.replace('ˈ', '').replace('ˌ', '').replace('‿', '').replace("'", ""))
+        for word in target_ipa_by_word_str
+    ]
+    # target_words_original 已經在上面被正確賦值了
+
+    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}")
+    
+    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]).replace(' ', '')
+
+    word_alignments = _get_phoneme_alignments_by_word(user_ipa_full, target_ipa_by_word)
+
+    return _format_to_json_structure(word_alignments, target_sentence, target_words_original)
+
+
+# --- 3. 對齊函數 (已簡化切分邏輯) ---
+def _get_phoneme_alignments_by_word(user_phoneme_str, target_words_ipa_tokenized):
+    """
+    執行音素對齊。對法語使用簡單的字元切分。
+    """
+    # 對於 user 的音素字串，也使用簡單的字元切分
+    user_phonemes = _tokenize_unicode_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. 格式化函數 (語言無關，保持不變) ---
+def _format_to_json_structure(alignments, sentence, original_words) -> dict:
+    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):
+            # 確保這裡也移除相關符號
+            missed_word_ipa_str = phonemize(original_words[i], language='fr-fr', backend='espeak', strip=True).replace('ˈ', '').replace('ˌ', '').replace('‿', '')
+            missed_word_ipa = _tokenize_unicode_ipa(missed_word_ipa_str)
+            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

@@ -7,4 +7,6 @@ soundfile
 librosa
 transformers
 phonemizer[espeak]
-numpy
+numpy
+epitran
+g2p