predict_model.py

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)