Upload predict_model.py

predict_model.py

@@ -0,0 +1,71 @@
+from tensorflow import keras
+import keras.layers
+import librosa
+import numpy as np
+import tensorflow as tf
+
+frame_length = 256
+frame_step = 160
+fft_length = 384
+
+
+def CTCLoss(y_true, y_pred):
+    batch_len = tf.cast(tf.shape(y_true)[0], dtype="int64")
+    input_length = tf.cast(tf.shape(y_pred)[1], dtype="int64")
+    label_length = tf.cast(tf.shape(y_true)[1], dtype="int64")
+
+    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype="int64")
+    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype="int64")
+
+    loss = keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)
+    return loss
+
+
+# Tải mô hình
+loaded_model = keras.models.load_model(r'D:\MyCode\Python\saved_model\my_model.h5', custom_objects={'CTCLoss': CTCLoss})
+
+characters = [x for x in "abcdefghijklmnopqrstuvwxyzăâêôơưđ'?! "]
+char_to_num = keras.layers.StringLookup(vocabulary=characters, oov_token="")
+num_to_char = keras.layers.StringLookup(vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True)
+
+
+def decode_batch_predictions(pred):
+    input_len = np.ones(pred.shape[0]) * pred.shape[1]
+    results = keras.backend.ctc_decode(pred, input_len=input_len, greedy=True)[0][0]
+    output_texts = []
+    for result in results:
+        result = tf.strings.reduce_join(num_to_char(result)).numpy().decode('utf-8')
+        output_texts.append(result)
+    return output_texts
+
+
+# Hàm để xử lý và dự đoán cho một tệp âm thanh
+def predict_from_audio(file_name):
+    # Tiền xử lý tệp âm thanh
+    audio, _ = librosa.load(file_name, sr=None)  # Đọc tệp âm thanh
+    audio = tf.convert_to_tensor(audio, dtype=tf.float32)
+
+    # Tính toán spectrogram
+    spectrogram = tf.signal.stft(audio, frame_length=frame_length, frame_step=frame_step, fft_length=fft_length)
+    spectrogram = tf.abs(spectrogram)
+    spectrogram = tf.math.pow(spectrogram, 0.5)
+
+    # Chuẩn hóa
+    mean = tf.math.reduce_mean(spectrogram, axis=1, keepdims=True)
+    stddevs = tf.math.reduce_std(spectrogram, axis=1, keepdims=True)
+    spectrogram = (spectrogram - mean) / (stddevs + 1e-10)
+
+    # Thêm chiều cho "channels" và "batch"
+    spectrogram = tf.expand_dims(spectrogram, axis=-1)  # Thêm chiều cho kênh
+    spectrogram = tf.expand_dims(spectrogram, axis=0)  # Thêm chiều batch
+
+    # Dự đoán
+    predictions = loaded_model.predict(spectrogram)
+    decoded_predictions = decode_batch_predictions(predictions)
+
+    return decoded_predictions
+
+
+# Dự đoán cho một tệp âm thanh
+result = predict_from_audio(r'D:\MyCode\Python\dataset\test_audio.wav')
+print("Dự đoán:", result)