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https://github.com/RVC-Boss/GPT-SoVITS.git
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143 lines
5.8 KiB
Python
143 lines
5.8 KiB
Python
import argparse
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import os
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import torchaudio
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import torchaudio.transforms as T
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import platform
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import Ref_Audio_Selector.config_param.config_params as params
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import Ref_Audio_Selector.config_param.log_config as log_config
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from Ref_Audio_Selector.common.time_util import timeit_decorator
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from Ref_Audio_Selector.common.model_manager import speaker_verification_models as models
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from modelscope.pipelines import pipeline
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def init_model(model_type='speech_campplus_sv_zh-cn_16k-common'):
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log_config.logger.info(f'人声识别模型类型：{model_type}')
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return pipeline(
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task=models[model_type]['task'],
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model=models[model_type]['model'],
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model_revision=models[model_type]['model_revision']
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)
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@timeit_decorator
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def compare_audio_and_generate_report(reference_audio_path, comparison_dir_path, output_file_path, model_type):
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sv_pipeline = init_model(model_type)
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# Step 1: 获取比较音频目录下所有音频文件的路径
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comparison_audio_paths = [os.path.join(comparison_dir_path, f) for f in os.listdir(comparison_dir_path) if
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f.endswith('.wav')]
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if platform.system() == 'Windows':
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# 因为这个模型是基于16k音频数据训练的，为了避免后续比较时，每次都对参考音频进行重采样，所以，提前进行了采样
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# windows不支持torchaudio.sox_effects.apply_effects_tensor，所以改写了依赖文件中的重采样方法
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# 改用torchaudio.transforms.Resample进行重采样，如果在非windows环境下，没有更改依赖包的采样方法的话，
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# 使用这段代码进行预采样会出现因为采样方法不同，而导致的模型相似度计算不准确的问题
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# 当然如果在windows下，使用了其他的采样方法，也会出现不准确的问题
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if params.enable_pre_sample == 'true':
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reference_audio_16k = ensure_16k_wav(reference_audio_path)
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else:
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reference_audio_16k = reference_audio_path
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else:
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reference_audio_16k = reference_audio_path
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# Step 2: 用参考音频依次比较音频目录下的每个音频，获取相似度分数及对应路径
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all_count = len(comparison_audio_paths)
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has_processed_count = 0
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similarity_scores = []
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for audio_path in comparison_audio_paths:
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score = sv_pipeline([reference_audio_16k, audio_path])['score']
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similarity_scores.append({
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'score': score,
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'path': audio_path
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})
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has_processed_count += 1
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log_config.logger.info(f'进度：{has_processed_count}/{all_count}')
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# Step 3: 根据相似度分数降序排列
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similarity_scores.sort(key=lambda x: x['score'], reverse=True)
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# Step 4: 处理输出文件不存在的情况，创建新文件
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if not os.path.exists(output_file_path):
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open(output_file_path, 'w').close() # Create an empty file
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# Step 5: 将排序后的结果写入输出结果文件（支持中文）
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formatted_scores = [f'{item["score"]}|{item["path"]}' for item in similarity_scores]
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with open(output_file_path, 'w', encoding='utf-8') as f:
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# 使用'\n'将每个字符串分开，使其写入不同行
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content = '\n'.join(formatted_scores)
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f.write(content)
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def ensure_16k_wav(audio_file_path, target_sample_rate=16000):
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"""
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输入一个音频文件地址，判断其采样率并决定是否进行重采样，然后将结果保存到指定的输出文件。
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参数:
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audio_file_path (str): 音频文件路径。
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output_file_path (str): 保存重采样后音频数据的目标文件路径。
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target_sample_rate (int, optional): 目标采样率，默认为16000Hz。
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"""
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# 读取音频文件并获取其采样率
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waveform, sample_rate = torchaudio.load(audio_file_path)
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# 判断是否需要重采样
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if sample_rate == target_sample_rate:
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return audio_file_path
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else:
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# 创建Resample实例
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resampler = T.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
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# 应用重采样
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resampled_waveform = resampler(waveform)
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# 创建临时文件夹
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os.makedirs(params.temp_dir, exist_ok=True)
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# 设置临时文件名
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temp_file_path = os.path.join(params.temp_dir, os.path.basename(audio_file_path))
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# 保存重采样后的音频到指定文件
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torchaudio.save(temp_file_path, resampled_waveform, target_sample_rate)
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return temp_file_path
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def parse_arguments():
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parser = argparse.ArgumentParser(description="Audio processing script arguments")
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# Reference audio path
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parser.add_argument("-r", "--reference_audio", type=str, required=True,
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help="Path to the reference WAV file.")
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# Comparison directory path
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parser.add_argument("-c", "--comparison_dir", type=str, required=True,
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help="Path to the directory containing comparison WAV files.")
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# Output file path
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parser.add_argument("-o", "--output_file", type=str, required=True,
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help="Path to the output file where results will be written.")
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# Model Type
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parser.add_argument("-m", "--model_type", type=str, required=True,
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help="Path to the model type.")
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return parser.parse_args()
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if __name__ == '__main__':
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cmd = parse_arguments()
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compare_audio_and_generate_report(
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reference_audio_path=cmd.reference_audio,
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comparison_dir_path=cmd.comparison_dir,
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output_file_path=cmd.output_file,
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model_type=cmd.model_type,
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)
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# compare_audio_and_generate_report(
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# reference_audio_path="D:/tt/渡鸦/refer_audio_all/也对，你的身份和我们不同吗？.wav",
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# comparison_dir_path='D:/tt/渡鸦/refer_audio_all',
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# output_file_path='D:/tt/渡鸦/test.txt',
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# )
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