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99 lines
3.3 KiB
99 lines
3.3 KiB
from resemblyzer import preprocess_wav, VoiceEncoder
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from pathlib import Path
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import numpy as np
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# 初始化声纹编码器
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encoder = VoiceEncoder()
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# 存储已知声纹和对应的说话人名称
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known_speakers = {}
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def load_known_speakers(speaker_folder):
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"""
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加载已知说话人的声纹
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:param speaker_folder: 存储说话人音频文件的文件夹路径
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"""
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speaker_folder = Path(speaker_folder)
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for speaker_dir in speaker_folder.iterdir():
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if speaker_dir.is_dir():
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speaker_name = speaker_dir.name
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wav_files = list(speaker_dir.glob("*.wav"))
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embeddings = []
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for wav_file in wav_files:
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wav = preprocess_wav(wav_file)
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embedding = encoder.embed_utterance(wav)
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embeddings.append(embedding)
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if embeddings:
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known_speakers[speaker_name] = np.mean(embeddings, axis=0)
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print(f"已加载说话人: {speaker_name}")
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def recognize_speaker_from_file(audio_file_path, threshold=0.7):
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"""
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从音频文件识别说话人
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:param audio_file_path: 音频文件路径
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:param threshold: 相似度阈值，低于此值认为是未知说话人
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:return: (说话人名称, 相似度)
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"""
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try:
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# 预处理音频文件
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wav = preprocess_wav(audio_file_path)
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# 获取声纹特征
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embedding = encoder.embed_utterance(wav)
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best_similarity = -1
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best_speaker = None
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# 与已知说话人进行比对
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for speaker, known_embedding in known_speakers.items():
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similarity = np.dot(embedding, known_embedding)
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if similarity > best_similarity:
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best_similarity = similarity
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best_speaker = speaker
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# 如果相似度低于阈值，认为是未知说话人
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if best_similarity < threshold:
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return "未知说话人", best_similarity
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return best_speaker, best_similarity
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except Exception as e:
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print(f"识别说话人时出错: {str(e)}")
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return None, 0
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def recognize_speaker(audio):
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"""
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识别说话人
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:param audio: 音频数据
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:return: 说话人名称
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"""
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wav = preprocess_wav(audio.get_wav_data())
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embedding = encoder.embed_utterance(wav)
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best_similarity = -1
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best_speaker = None
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for speaker, known_embedding in known_speakers.items():
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similarity = np.dot(embedding, known_embedding)
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if similarity > best_similarity:
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best_similarity = similarity
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best_speaker = speaker
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return best_speaker
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if __name__ == "__main__":
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# 加载已知说话人的声纹
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speaker_folder = "known_speakers" # 已知说话人音频文件夹
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load_known_speakers(speaker_folder)
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# 要识别的音频文件路径
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#test_audio_path = "huanghai_test.wav" # 替换为你要识别的音频文件路径
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test_audio_path = "wubin_test.wav" # 替换为你要识别的音频文件路径
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# 识别说话人
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speaker, similarity = recognize_speaker_from_file(test_audio_path)
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if speaker:
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print(f"识别结果: {speaker}")
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print(f"相似度: {similarity:.4f}")
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else:
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print("无法识别说话人") |