Create app.py

app.py

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+import gradio as gr
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import librosa
+import torch
+import epitran
+import re
+import difflib
+import editdistance
+from jiwer import wer
+import json
+
+# Load model once at startup
+model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic"
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+epi = epitran.Epitran('ara-Arab')
+
+def clean_phonemes(ipa):
+    """Remove diacritics and length markers from phonemes"""
+    return re.sub(r'[\u064B-\u0652\u02D0]', '', ipa)
+
+def analyze_phonemes(language, reference_text, audio_file):
+    # Convert reference text to phonemes
+    ref_phonemes = []
+    for word in reference_text.split():
+        ipa = epi.transliterate(word)
+        ipa_clean = clean_phonemes(ipa)
+        ref_phonemes.append(list(ipa_clean))
+    
+    # Process audio file
+    audio, sr = librosa.load(audio_file.name, sr=16000)
+    input_values = processor(audio, sampling_rate=16000, return_tensors="pt").input_values
+    
+    # Get transcription
+    with torch.no_grad():
+        logits = model(input_values).logits
+        pred_ids = torch.argmax(logits, dim=-1)
+        transcription = processor.batch_decode(pred_ids)[0].strip()
+    
+    # Convert transcription to phonemes
+    obs_phonemes = []
+    for word in transcription.split():
+        ipa = epi.transliterate(word)
+        ipa_clean = clean_phonemes(ipa)
+        obs_phonemes.append(list(ipa_clean))
+    
+    # Prepare results in JSON format
+    results = {
+        "reference_text": reference_text,
+        "transcription": transcription,
+        "word_alignment": [],
+        "metrics": {}
+    }
+    
+    # Calculate metrics
+    total_phoneme_errors = 0
+    total_phoneme_length = 0
+    correct_words = 0
+    total_word_length = len(ref_phonemes)
+    
+    # Word-by-word alignment
+    for i, (ref, obs) in enumerate(zip(ref_phonemes, obs_phonemes)):
+        ref_str = ''.join(ref)
+        obs_str = ''.join(obs)
+        edits = editdistance.eval(ref, obs)
+        acc = round((1 - edits / max(1, len(ref))) * 100, 2)
+        
+        # Get error details
+        matcher = difflib.SequenceMatcher(None, ref, obs)
+        ops = matcher.get_opcodes()
+        error_details = []
+        for tag, i1, i2, j1, j2 in ops:
+            ref_seg = ''.join(ref[i1:i2]) or '-'
+            obs_seg = ''.join(obs[j1:j2]) or '-'
+            if tag != 'equal':
+                error_details.append({
+                    "type": tag.upper(),
+                    "reference": ref_seg,
+                    "observed": obs_seg
+                })
+        
+        results["word_alignment"].append({
+            "word_index": i,
+            "reference_phonemes": ref_str,
+            "observed_phonemes": obs_str,
+            "edit_distance": edits,
+            "accuracy": acc,
+            "is_correct": edits == 0,
+            "errors": error_details
+        })
+        
+        total_phoneme_errors += edits
+        total_phoneme_length += len(ref)
+        correct_words += 1 if edits == 0 else 0
+    
+    # Calculate metrics
+    phoneme_acc = round((1 - total_phoneme_errors / max(1, total_phoneme_length)) * 100, 2)
+    phoneme_er = round((total_phoneme_errors / max(1, total_phoneme_length)) * 100, 2)
+    word_acc = round((correct_words / max(1, total_word_length)) * 100, 2)
+    word_er = round(((total_word_length - correct_words) / max(1, total_word_length)) * 100, 2)
+    text_wer = round(wer(reference_text, transcription) * 100, 2)
+    
+    results["metrics"] = {
+        "word_accuracy": word_acc,
+        "word_error_rate": word_er,
+        "phoneme_accuracy": phoneme_acc,
+        "phoneme_error_rate": phoneme_er,
+        "asr_word_error_rate": text_wer
+    }
+    
+    return json.dumps(results, indent=2, ensure_ascii=False)
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=analyze_phonemes,
+    inputs=[
+        gr.Dropdown(["Arabic"], label="Language", value="Arabic"),
+        gr.Textbox(label="Reference Text", value="فَبِأَيِّ آلَاءِ رَبِّكُمَا تُكَذِّبَانِ"),
+        gr.File(label="Upload Audio File", type="file")
+    ],
+    outputs=gr.JSON(label="Phoneme Alignment Results"),
+    title="Arabic Phoneme Alignment Analysis",
+    description="Compare audio pronunciation with reference text at phoneme level"
+)
+
+demo.launch()