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app_v4.py

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+import gradio as gr
+import requests
+import Levenshtein
+import librosa
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+def load_model():
+    MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic"
+    processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
+    model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+    return processor, model
+
+processor, model = load_model()
+
+def transcribe_audio_hf(audio_path):
+    """
+    Transcribes speech from an audio file using a pretrained Wav2Vec2 model.
+    Args:
+        audio_path (str): Path to the audio file.
+    Returns:
+        str: The transcription of the speech in the audio file.
+    """
+    speech_array, sampling_rate = librosa.load(audio_path, sr=16000)
+    input_values = processor(speech_array, sampling_rate=sampling_rate, return_tensors="pt", padding=True).input_values
+    with torch.no_grad():
+        logits = model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)[0].strip()
+    return transcription
+
+def levenshtein_similarity(transcription1, transcription2):
+    """
+    Calculate the Levenshtein similarity between two transcriptions.
+    Args:
+        transcription1 (str): The first transcription.
+        transcription2 (str): The second transcription.
+    Returns:
+        float: A normalized similarity score between 0 and 1, where 1 indicates identical transcriptions.
+    """
+    distance = Levenshtein.distance(transcription1, transcription2)
+    max_len = max(len(transcription1), len(transcription2))
+    return 1 - distance / max_len  # Normalize to get similarity score
+
+def evaluate_audio_similarity(original_audio, user_audio):
+    """
+    Compares the similarity between the transcription of an original audio file and a user's audio file.
+    Args:
+        original_audio (str): Path to the original audio file.
+        user_audio (str): Path to the user's audio file.
+    Returns:
+        tuple: Transcriptions and Levenshtein similarity score.
+    """
+    transcription_original = transcribe_audio_hf(original_audio)
+    transcription_user = transcribe_audio_hf(user_audio)
+    similarity_score_levenshtein = levenshtein_similarity(transcription_original, transcription_user)
+    return transcription_original, transcription_user, similarity_score_levenshtein
+
+def perform_testing(original_audio, user_audio):
+    if original_audio is not None and user_audio is not None:
+        transcription_original, transcription_user, similarity_score = evaluate_audio_similarity(original_audio, user_audio)
+        return (
+            f"**Original Transcription:** {transcription_original}",
+            f"**User Transcription:** {transcription_user}",
+            f"**Levenshtein Similarity Score:** {similarity_score:.2f}"
+        )
+
+# Gradio Interface
+with gr.Blocks() as app:
+    gr.Markdown("# Audio Transcription and Similarity Checker")
+
+    original_audio_upload = gr.Audio(label="Upload Original Audio", type="filepath")
+    user_audio_upload = gr.Audio(label="Upload User Audio", type="filepath")
+    upload_button = gr.Button("Perform Testing")
+    output_original_transcription = gr.Markdown()
+    output_user_transcription = gr.Markdown()
+    output_similarity_score = gr.Markdown()
+
+    upload_button.click(
+        perform_testing,
+        inputs=[original_audio_upload, user_audio_upload],
+        outputs=[output_original_transcription, output_user_transcription, output_similarity_score]
+    )
+
+app.launch()

requirements.txt

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+transformers[torch] 
+pydub 
+Levenshtein 
+av 
+librosa
+gradio