import os import keras.layers import librosa import matplotlib.pyplot as plt import numpy as np import pandas as pd from jiwer import wer from keras.src.applications.densenet import layers from scipy.io import wavfile import tensorflow as tf data_path = r"D:\MyCode\Python\dataset\LJSpeech-1.1" wave_path = data_path + "/wavs/" metadata_path = data_path + '/metadata.csv' metadata_df = pd.read_csv(metadata_path, sep="|", header=None, quoting=3) metadata_df.columns = ["file_name", "transcription", "normalized_transcription"] metadata_df = metadata_df[["file_name", "transcription", "normalized_transcription"]] metadata_df = metadata_df.sample(frac=1).reset_index(drop=True) print(metadata_df.head(10)) split = int(len(metadata_df) * 0.90) df_train = metadata_df[:split] df_test = metadata_df[split:] frame_length = 256 frame_step = 160 fft_length = 384 batch_size = 32 epochs = 10 # preprocessing 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 encode_single_sample(wav_file, label): # file = tf.io.read_file(wave_path, wav_file + ".wav") # audio, _ = tf.audio.decode_wav(file) # audio = tf.squeeze(audio, axis=-1) # audio = tf.cast(audio, tf.float32) # # 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) # # mean = tf.math.reduce_mean(spectrogram, 1, keepdims=True) # stddevs = tf.math.reduce_std(spectrogram, 1, keepdims=True) # spectrogram = (spectrogram - mean) / (stddevs + 1e-10) # # label = tf.strings.lower(label) # label = tf.strings.unicode_split(label, input_encoding='UTF-8') # label = char_to_num(label) # return spectrogram, label def encode_single_sample(wav_file, label): # Tạo đường dẫn file âm thanh file_path = tf.strings.join([wave_path, wav_file, ".wav"], separator="") # Đọc file âm thanh file = tf.io.read_file(file_path) audio, _ = tf.audio.decode_wav(file) audio = tf.squeeze(audio, axis=-1) audio = tf.cast(audio, 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" spectrogram = tf.expand_dims(spectrogram, axis=-1) # Giữ nguyên spectrogram = tf.expand_dims(spectrogram, axis=0) # Thêm chiều batch # Xử lý nhãn label = tf.strings.lower(label) label = tf.strings.unicode_split(label, input_encoding='UTF-8') label = char_to_num(label) return spectrogram, label train_dataset = tf.data.Dataset.from_tensor_slices(( list(df_train["file_name"]), list(df_train["normalized_transcription"]) )) train_dataset = ( train_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE) .padded_batch(batch_size) .prefetch(buffer_size=tf.data.AUTOTUNE) ) # Tạo dataset cho validation validation_dataset = tf.data.Dataset.from_tensor_slices(( list(df_test["file_name"]), list(df_test["normalized_transcription"]) )) validation_dataset = ( validation_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE) .padded_batch(batch_size) .prefetch(buffer_size=tf.data.AUTOTUNE) ) for batch in train_dataset.take(1): spectrogram = batch[0][0].numpy() # Lấy spectrogram từ batch # Kiểm tra kích thước if spectrogram.ndim == 4: # Nếu là mảng 4D, loại bỏ chiều batch spectrogram = tf.squeeze(spectrogram, axis=0) # Kiểm tra lại nếu là mảng 3D if spectrogram.ndim == 3: # Nếu vẫn là mảng 3D, chuyển đổi về mảng 2D spectrogram = np.squeeze(spectrogram, axis=-1) # Chuyển đổi về mảng 2D # Áp dụng np.trim_zeros cho từng hàng trimmed_spectrogram = [np.trim_zeros(x) for x in spectrogram.T] # Chuyển vị và trim # Chuyển đổi về numpy array 2D nếu cần max_length = max(len(x) for x in trimmed_spectrogram) # Tìm chiều dài tối đa trimmed_spectrogram = np.array([np.pad(x, (0, max_length - len(x)), mode='constant') for x in trimmed_spectrogram]) 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 def build_model(input_dim, output_dim, rnn_layer=5, rnn_units=128): input_spectrogram = layers.Input(shape=(None, input_dim), name="input") x = layers.Reshape((-1, input_dim, 1), name="expand_dim")(input_spectrogram) # Lớp Convolutional 1 x = layers.Conv2D(filters=32, kernel_size=[11, 41], strides=[2, 2], padding="same", use_bias=False, name="conv_1")(x) x = layers.BatchNormalization(name="bn_conv_1")(x) # Đổi tên lớp này x = layers.ReLU(name="relu_1")(x) # Lớp Convolutional 2 x = layers.Conv2D(filters=32, kernel_size=[11, 21], strides=[1, 2], padding="same", use_bias=False, name="conv_2")(x) x = layers.BatchNormalization(name="bn_conv_2")(x) # Đổi tên lớp này x = layers.ReLU(name="relu_2")(x) # Reshape để sử dụng với RNN x = layers.Reshape((-1, x.shape[-2] * x.shape[-1]))(x) for i in range(1, rnn_layer + 1): recurrent = layers.GRU( units=rnn_units, activation="tanh", recurrent_activation="sigmoid", use_bias=True, return_sequences=True, reset_after=True, name=f"gru_{i}", ) # Các lớp Recurrent x = layers.Bidirectional( recurrent, name=f"bidirectional_{i}", merge_mode="concat", )(x) if i < rnn_layer: x = layers.Dropout(rate=0.5)(x) x = layers.Dense(units=rnn_units * 2, name="dense_1")(x) x = layers.ReLU(name="relu_3")(x) x = layers.Dropout(rate=0.5)(x) output = layers.Dense(units=output_dim + 1, activation="softmax")(x) model = keras.Model(input_spectrogram, output, name="DeepSpeech_2") otp = keras.optimizers.Adam(learning_rate=1e-4) model.compile(optimizer=otp, loss=CTCLoss) return model model = build_model( input_dim=fft_length // 2 + 1, output_dim=char_to_num.vocab_size(), rnn_units=512, ) model.summary() 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 class CallbackEval(keras.callbacks.Callback): def __init__(self, dataset): super().__init__() self.dataset = dataset def on_epoch_end(self, epoch, logs=None): prediction = [] targets = [] for batch in self.dataset: X, y = batch batch_predictions = model.predict(X) batch_predictions = decode_batch_predictions(batch_predictions) prediction.extend(batch_predictions) for label in y: label = (tf.strings.reduce_join(num_to_char(label)).numpy().decode("utf-8")) targets.append(label) wer_score = wer(targets, prediction) print(f"WER: {wer_score:.4f}") for i in np.random.randint(0, len(prediction), 2): print(f"Target: {targets[i]}") print(f"Prediction: {prediction[i]}") validation_callback = CallbackEval(validation_dataset) history = model.fit( train_dataset, validation_data=validation_dataset, epochs=epochs, callbacks=[validation_callback], ) model.save(r'D:\MyCode\Python\pythonProject\SavedModed\model_stt.h5')