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CL-KWS_202408_v1
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import os, warnings, argparse
import tensorflow as tf
import numpy as np
from model import ukws
from dataset import dataloader_demo
import gradio as gr
# import librosa

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
warnings.filterwarnings('ignore')
warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning) 
# np.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)
warnings.simplefilter("ignore")

seed = 42
tf.random.set_seed(seed)
np.random.seed(seed)


parser = argparse.ArgumentParser()

parser.add_argument('--text_input', required=False, type=str, default='g2p_embed')
parser.add_argument('--audio_input', required=False, type=str, default='both')
parser.add_argument('--load_checkpoint_path', default='/share/nas165/yiting/CL-KWS_202408_v1/checkpoint_results/checkpoint_guided_ctc/20240725-011006')
parser.add_argument('--keyword_list_length', default=8, type=int)
# parser.add_argument('--load_checkpoint_path', required=True, type=str)
# parser.add_argument('--keyword_list_length', required=True, type=int)
parser.add_argument('--stack_extractor', action='store_true')
parser.add_argument('--comment', required=False, type=str)
args = parser.parse_args()

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
    except RuntimeError as e:
        print(e)

strategy = tf.distribute.MirroredStrategy()
batch_size = args.keyword_list_length
# Batch size per GPU
GLOBAL_BATCH_SIZE = batch_size * strategy.num_replicas_in_sync
# BATCH_SIZE_PER_REPLICA = GLOBAL_BATCH_SIZE / strategy.num_replicas_in_sync

# Make Dataloader
text_input = args.text_input
audio_input = args.audio_input
load_checkpoint_path = args.load_checkpoint_path

phonemes = ["<pad>", ] + ['AA0', 'AA1', 'AA2', 'AE0', 'AE1', 'AE2', 'AH0', 'AH1', 'AH2', 'AO0',
                                        'AO1', 'AO2', 'AW0', 'AW1', 'AW2', 'AY0', 'AY1', 'AY2', 'B', 'CH', 
                                        'D', 'DH', 'EH0', 'EH1', 'EH2', 'ER0', 'ER1', 'ER2', 'EY0', 'EY1', 
                                        'EY2', 'F', 'G', 'HH', 'IH0', 'IH1', 'IH2', 'IY0', 'IY1', 'IY2', 
                                        'JH', 'K', 'L', 'M', 'N', 'NG', 'OW0', 'OW1', 'OW2', 'OY0', 
                                        'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH0', 'UH1', 
                                        'UH2', 'UW', 'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH', 
                                        ' ']
# Number of phonemes
vocab = len(phonemes)

# Model params.
kwargs = {
        'vocab' : vocab,
        'text_input' : text_input,
        'audio_input' : audio_input,
        'frame_length' : 400, 
        'hop_length' : 160, 
        'num_mel'  : 40, 
        'sample_rate' : 16000,
        'log_mel' : False,
        'stack_extractor' : args.stack_extractor,
    }



# Make tensorboard dict.
global keyword
param = kwargs
param['comment'] = args.comment


with strategy.scope():
    

    model = ukws.BaseUKWS(**kwargs)
    if args.load_checkpoint_path:
        checkpoint_dir=args.load_checkpoint_path
        checkpoint = tf.train.Checkpoint(model=model)
        checkpoint_manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=5)
        latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
        if latest_checkpoint:
            checkpoint.restore(latest_checkpoint)
            print("Checkpoint restored!")
        else:
            print("No checkpoint found.")
            
    def inference(audio,keyword):
        
        if isinstance(keyword, str):
            keyword = [kw.strip() for kw in keyword.split(',')]        
        
        test_google_dataset = dataloader_demo.GoogleCommandsDataloader(batch_size=GLOBAL_BATCH_SIZE, features=text_input, wav_path_or_object=audio, keyword = keyword)

        test_google_dataset = dataloader_demo.convert_sequence_to_dataset(test_google_dataset)

        test_google_dist_dataset = strategy.experimental_distribute_dataset(test_google_dataset)

        
        # @tf.function
        def test_step_metric_only(inputs,keyword_list):
            clean_speech = inputs[0]
            text = inputs[1]
            labels = inputs[2]
            prob, affinity_matrix = model(clean_speech, text, training=False)[:2]
            prob=tf.round(prob * 1000) / 1000
            prob = prob.numpy().flatten()
            max_indices = np.argmax(prob,axis=0)
            if prob[max_indices] >= 0.8:
                keyword = keyword_list[ max_indices]
            else :
                keyword = 'no keyword'
            
            print('keyword:',keyword_list)
            print('prob',prob)
            msg = ''
            for k, p in zip(keyword_list, prob):
                msg += '{} | {:.2f} \n'.format(k, p)

            return keyword, msg

        for x in test_google_dist_dataset:
            keyword, prob = test_step_metric_only(x,keyword)
        
         
        return keyword, prob

    # keyword = ['realtek go','ok google','vintage','hackney','crocodile','surroundings','oversaw','northwestern']
    # audio = '/share/nas165/yiting/recording/ok_google/Default_20240725-183000.wav'
    # inference(audio,keyword)
    
    demo = gr.Interface(
        fn=inference,
        inputs=[gr.Audio(source="upload", label="Sound"),
                gr.Textbox(placeholder="Keyword List Here...", label="keyword_list")],
        examples=[
        ["./recording/ok_google/ok_google-183000.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/ok_google/ok_google-183005.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/ok_google/ok_google-183008.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/ok_google/ok_google-183011.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/ok_google/ok_google-183015.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/realtek_go/realtek_go-183029.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/realtek_go/realtek_go-183033.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/realtek_go/realtek_go-183036.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/realtek_go/realtek_go-183039.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ["./recording/realtek_go/realtek_go-183043.wav", 'realtek go,ok google,vintage,hackney,crocodile,surroundings,oversaw,northwestern'],
        ],
        outputs=[gr.Textbox(label="keyword"), gr.Textbox(label="Confidence Score of keyword")],
        )

    demo.launch(server_name='0.0.0.0', server_port=7860,share=True)