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jblitzar
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github-python

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# cv2.imshow(f"Frame {frame_index}", frame)
# -------- SenceVoice 推理 ---------
input_file = (TEMP_AUDIO_FILE)
res = model_senceVoice.generate(
input=input_file,
cache={},
language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
use_itn=False,
)
prompt = res[0]['text'].split(">")[-1]
# ---------SenceVoice --end----------
# -------- QWen2-VL 模型推理 ---------
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": f"{file_path}",
},
{"type": "text", "text": f"{prompt}"},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
# 输入文本
text = output_text[0]
# asyncio.run(amain(text, "zh-CN-YunxiaNeural", os.path.join(folder_path,"sft_0.mp3")))
# play_audio(f'{folder_path}/sft_0.mp3')
asyncio.run(amain(text, "zh-CN-XiaoyiNeural", os.path.join(folder_path,"sft_0.mp3")))
play_audio(f'{folder_path}/sft_0.mp3')
# asyncio.run(amain(text, "zh-CN-YunjianNeural", os.path.join(folder_path,"sft_0.mp3")))
# play_audio(f'{folder_path}/sft_0.mp3')
# asyncio.run(amain(text, "zh-CN-shaanxi-XiaoniNeural", os.path.join(folder_path,"sft_0.mp3")))
# play_audio(f'{folder_path}/sft_0.mp3')
# <FILESEP>
"""
Computes the Underwater Image Quality Measure (UIQM)
metrics paper: https://ieeexplore.ieee.org/document/7305804
referenced from https://github.com/xahidbuffon/FUnIE-GAN/blob/master/Evaluation/uqim_utils.py
"""
from scipy import ndimage
from PIL import Image
import numpy as np
import math
def mu_a(x, alpha_L=0.1, alpha_R=0.1):
"""
Calculates the asymetric alpha-trimmed mean
"""
# sort pixels by intensity - for clipping
x = sorted(x)
# get number of pixels
K = len(x)
# calculate T alpha L and T alpha R
T_a_L = math.ceil(alpha_L * K)
T_a_R = math.floor(alpha_R * K)
# calculate mu_alpha weight
weight = (1 / (K - T_a_L - T_a_R))
# loop through flattened image starting at T_a_L+1 and ending at K-T_a_R
s = int(T_a_L + 1)
e = int(K - T_a_R)
val = sum(x[s:e])
val = weight * val
return val
def s_a(x, mu):
val = 0
for pixel in x: