https://github.com/pyannote/pyannote-audio
代码语言：javascript复制pip install pyannote.audio场景：
- 一段音频中有多个说话人，将不同的人说的话分离出来
- 已知一些人的语音特征，跟分离出来的片段，分别求特征的余弦距离，余弦距离最小的作为说话的人
# _*_ coding: utf-8 _*_
# @Time : 2024/3/16 10:47
# @Author : Michael
# @File : spearker_rec.py
# @desc :
import torch
from pyannote.audio import Model, Pipeline, Inference
from pyannote.core import Segment
from scipy.spatial.distance import cosine
def extract_speaker_embedding(pipeline, audio_file, speaker_label):
diarization = pipeline(audio_file)
speaker_embedding = None
for turn, _, label in diarization.itertracks(yield_label=True):
if label == speaker_label:
segment = Segment(turn.start, turn.end)
speaker_embedding = inference.crop(audio_file, segment)
break
return speaker_embedding
# 对于给定的音频，提取声纹特征并与人库中的声纹进行比较
def recognize_speaker(pipeline, audio_file):
diarization = pipeline(audio_file)
speaker_turns = []
for turn, _, speaker_label in diarization.itertracks(yield_label=True):
# 提取切片的声纹特征
embedding = inference.crop(audio_file, turn)
distances = {}
for speaker, embeddings in speaker_embeddings.items():
# 计算与已知说话人的声纹特征的余弦距离
distances[speaker] = min([cosine(embedding, e) for e in embeddings])
# 选择距离最小的说话人
recognized_speaker = min(distances, key=distances.get)
speaker_turns.append((turn, recognized_speaker))
# 记录说话人的时间段和余弦距离最小的预测说话人
return speaker_turns
if __name__ == "__main__":
token = "hf_***" # 请替换为您的Hugging Face Token
# 加载声音分离识别模型
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization-3.1",
use_auth_token=token, # 在项目页面agree使用协议，并获取 Hugging Face Token
# cache_dir="/home/huggingface/hub/models--pyannote--speaker-diarization-3.1/"
)
# 加载声纹嵌入模型
embed_model = Model.from_pretrained("pyannote/embedding", use_auth_token=token)
inference = Inference(embed_model, window="whole")
# pipeline.to(torch.device("cuda"))
# 假设您已经有一个包含不同人声的音频文件集,以及对应的人
audio_files = {
"mick": "mick.wav", # mick的音频
"moon": "moon.wav", # moon的音频
}
speaker_embeddings = {}
for speaker, audio_file in audio_files.items():
diarization = pipeline(audio_file)
for turn, _, speaker_label in diarization.itertracks(yield_label=True):
embedding = extract_speaker_embedding(pipeline, audio_file, speaker_label)
# 获取原始已知说话人的声纹特征
speaker_embeddings.setdefault(speaker, []).append(embedding)
# 给定新的未知人物的音频文件
given_audio_file = "2_voice.wav" # 前半部分是 mick 说话，后半部分是 moon 说话
# 识别给定音频中的说话人
recognized_speakers = recognize_speaker(pipeline, given_audio_file)
print("Recognized speakers in the given audio:")
for turn, speaker in recognized_speakers:
print(f"Speaker {speaker} spoke between {turn.start:.2f}s and {turn.end:.2f}s")输出：
代码语言：javascript复制Model was trained with pyannote.audio 0.0.1, yours is 3.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.
Model was trained with torch 1.8.1 cu102, yours is 2.2.1 cpu. Bad things might happen unless you revert torch to 1.x.
Recognized speakers in the given audio:
Speaker mick spoke between 0.57s and 1.67s
Speaker moon spoke between 2.47s and 2.81s
Speaker moon spoke between 3.08s and 4.47s输出提示环境不太一样，需要注意一下
