Automatic Speech Recognition算法代码
时间: 2023-10-19 11:05:12 浏览: 44
以下是基于Python和Kaldi工具包的GMM-HMM自动语音识别算法代码示例:
```python
import kaldi_io
import numpy as np
from kaldi.feat.mfcc import Mfcc, MfccOptions
from kaldi.feat.functions import compute_cmvn_stats
from kaldi.hmm import HmmTopology, MakeAmDiagGmm, DecodableInterface, DecodableMatrixScaled
from kaldi.matrix import SubVector, Vector
from kaldi.util.table import SequentialMatrixReader, SequentialWaveReader
from kaldi.fstext import SymbolTable, ReadFstKaldiGeneric
from kaldi.decoder import LatticeFasterDecoder, CompactLatticeWriter
# MFCC特征提取
def extract_mfcc(signal, sample_rate, num_mel_bins, num_ceps, use_energy=True, use_delta=True):
mfcc_opts = MfccOptions()
mfcc_opts.mel_opts.num_bins = num_mel_bins
mfcc_opts.num_ceps = num_ceps
mfcc_opts.use_energy = use_energy
mfcc_opts.use_delta = use_delta
mfcc = Mfcc(mfcc_opts)
feats = mfcc.compute_features(signal, sample_rate)
return feats
# 计算CMVN统计量
def compute_cmvn_feats(feats):
stats = compute_cmvn_stats(feats)
feats_cmvn = feats.copy()
feats_cmvn.add_row(-stats.mean_vec)
feats_cmvn.scale_rows(1.0 / np.sqrt(stats.var_vec))
return feats_cmvn
# 构建HMM拓扑结构
def build_hmm_topology(num_pdfs):
topo = HmmTopology()
topo.add_transition(0, 1, 0)
for i in range(1, num_pdfs):
topo.add_transition(i, i + 1, 0)
topo.add_self_loop(1, 1)
topo.set_final(num_pdfs, 0)
return topo
# 训练GMM模型
def train_gmm(data_rspecifier, num_gaussians, num_iterations, num_frames_per_batch):
feats_rspec = f"ark,s,cs:apply-cmvn --norm-vars=false scp:{data_rspecifier} ark:- |"
feats_reader = SequentialMatrixReader(feats_rspec)
# 初始化GMM
feats = feats_reader[0][1]
dim = feats.shape[1]
gmm = MakeAmDiagGmm(num_gaussians, dim, 1)
# EM迭代
for i in range(num_iterations):
sum_accs = gmm.AccumulateForUtterance(feats, 1.0)
for j in range(1, feats_reader.num_rows()):
utt, utt_feats = feats_reader[j]
sum_accs.Add(gmm.AccumulateForUtterance(utt_feats, 1.0))
if (j + 1) % num_frames_per_batch == 0:
gmm.Update(sum_accs, "m", True)
sum_accs = gmm.ZeroAccs()
if not sum_accs.IsZero():
gmm.Update(sum_accs, "m", True)
return gmm
# 解码
def decode(gmm, transducer, feats, word_symbols):
decoder = LatticeFasterDecoder(transducer, 1.0, 0.0, True, 40.0, 30.0, 0.1, 0.1, True, True, True)
decodable = DecodableMatrixScaled(DecodableInterface(gmm, feats), 1.0)
# 解码
decoder.Decode(decodable)
# 获取最佳路径
lattice = decoder.GetRawLattice()
CompactLatticeWriter.Write("ark:| gzip -c > lat.gz", lattice)
# 转录
with open("lat.gz", "rb") as f:
for key, lat_str in kaldi_io.read_compress_archive(f):
lat = CompactLattice()
lat.ParseFromString(lat_str)
best_path = kaldi.fst.shortestpath.shortestpath(lat).olabels
words = [word_symbols.Find(sym).decode() for _, sym in best_path]
print(key, " ".join(words))
if __name__ == '__main__':
# 加载词典
word_symbols = SymbolTable.ReadText("words.txt")
# 加载HMM拓扑
topo = ReadFstKaldiGeneric("topo")
# 加载语言模型
graph = ReadFstKaldiGeneric("graph")
# 加载数据
data_rspecifier = "scp:data.scp"
# 提取MFCC特征
feats = extract_mfcc(signal, sample_rate, num_mel_bins=40, num_ceps=13)
# 计算CMVN统计量
feats_cmvn = compute_cmvn_feats(feats)
# 训练GMM
gmm = train_gmm(data_rspecifier, num_gaussians=2048, num_iterations=10, num_frames_per_batch=10000)
# 解码
decode(gmm, topo, feats_cmvn, graph, word_symbols)
```
该代码使用Kaldi工具包实现了一个基于GMM-HMM的自动语音识别系统,包括MFCC特征提取、CMVN归一化、GMM训练和解码等步骤。